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<div class="section" id="table-of-datasets">
<h1>Table of Datasets<a class="headerlink" href="#table-of-datasets" title="Permalink to this headline">¶</a></h1>
<p>Find a table of all 43 datasets available in matminer here.</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 70%" />
<col style="width: 10%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Name</p></th>
<th class="head"><p>Description</p></th>
<th class="head"><p>Entries</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">boltztrap_mp</span></code></p></td>
<td><p>Effective mass and thermoelectric properties of 8924 compounds in The Materials Project database that are calculated by the BoltzTraP software package run on the GGA-PBE or GGA+U density functional theory calculation results</p></td>
<td><p>8924</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">brgoch_superhard_training</span></code></p></td>
<td><p>2574 materials used for training regressors that predict shear and bulk modulus.</p></td>
<td><p>2574</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">castelli_perovskites</span></code></p></td>
<td><p>18,928 perovskites generated with ABX combinatorics, calculating gllbsc band gap and pbe structure, and also reporting absolute band edge positions and heat of formation.</p></td>
<td><p>18928</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">citrine_thermal_conductivity</span></code></p></td>
<td><p>Thermal conductivity of 872 compounds measured experimentally and retrieved from Citrine database from various references</p></td>
<td><p>872</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">dielectric_constant</span></code></p></td>
<td><p>1,056 structures with dielectric properties, calculated with DFPT-PBE.</p></td>
<td><p>1056</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">double_perovskites_gap</span></code></p></td>
<td><p>Band gap of 1306 double perovskites (a_1-b_1-a_2-b_2-O6) calculated using Gritsenko, van Leeuwen, van Lenthe and Baerends potential (gllbsc) in GPAW.</p></td>
<td><p>1306</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">double_perovskites_gap_lumo</span></code></p></td>
<td><p>Supplementary lumo data of 55 atoms for the double_perovskites_gap dataset.</p></td>
<td><p>55</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">elastic_tensor_2015</span></code></p></td>
<td><p>1,181 structures with elastic properties calculated with DFT-PBE.</p></td>
<td><p>1181</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">expt_formation_enthalpy</span></code></p></td>
<td><p>Experimental formation enthalpies for inorganic compounds, collected from years of calorimetric experiments</p></td>
<td><p>1276</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">expt_formation_enthalpy_kingsbury</span></code></p></td>
<td><p>Dataset containing experimental standard formation enthalpies for solids</p></td>
<td><p>2135</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">expt_gap</span></code></p></td>
<td><p>Experimental band gap of 6354 inorganic semiconductors.</p></td>
<td><p>6354</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">expt_gap_kingsbury</span></code></p></td>
<td><p>Identical to the matbench_expt_gap dataset, except that Materials Project database IDs (mp-ids) have been associated with each material using the same method as described for the expt_formation_enthalpy_kingsbury dataset</p></td>
<td><p>4604</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">flla</span></code></p></td>
<td><p>3938 structures and computed formation energies from “Crystal Structure Representations for Machine Learning Models of Formation Energies.”</p></td>
<td><p>3938</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">glass_binary</span></code></p></td>
<td><p>Metallic glass formation data for binary alloys, collected from various experimental techniques such as melt-spinning or mechanical alloying</p></td>
<td><p>5959</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">glass_binary_v2</span></code></p></td>
<td><p>Identical to glass_binary dataset, but with duplicate entries merged</p></td>
<td><p>5483</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">glass_ternary_hipt</span></code></p></td>
<td><p>Metallic glass formation dataset for ternary alloys, collected from the high-throughput sputtering experiments measuring whether it is possible to form a glass using sputtering</p></td>
<td><p>5170</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">glass_ternary_landolt</span></code></p></td>
<td><p>Metallic glass formation dataset for ternary alloys, collected from the “Nonequilibrium Phase Diagrams of Ternary Amorphous Alloys,’ a volume of the Landolt– Börnstein collection</p></td>
<td><p>7191</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">heusler_magnetic</span></code></p></td>
<td><p>1153 Heusler alloys with DFT-calculated magnetic and electronic properties</p></td>
<td><p>1153</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">jarvis_dft_2d</span></code></p></td>
<td><p>Various properties of 636 2D materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database.</p></td>
<td><p>636</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">jarvis_dft_3d</span></code></p></td>
<td><p>Various properties of 25,923 bulk materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database.</p></td>
<td><p>25923</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">jarvis_ml_dft_training</span></code></p></td>
<td><p>Various properties of 24,759 bulk and 2D materials computed with the OptB88vdW and TBmBJ functionals taken from the JARVIS DFT database.</p></td>
<td><p>24759</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">m2ax</span></code></p></td>
<td><p>Elastic properties of 223 stable M2AX compounds from “A comprehensive survey of M2AX phase elastic properties” by Cover et al</p></td>
<td><p>223</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_dielectric</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting refractive index from structure</p></td>
<td><p>4764</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_expt_gap</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting experimental band gap from composition alone</p></td>
<td><p>4604</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_expt_is_metal</span></code></p></td>
<td><p>Matbench v0.1 test dataset for classifying metallicity from composition alone</p></td>
<td><p>4921</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_glass</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting full bulk metallic glass formation ability from chemical formula</p></td>
<td><p>5680</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_jdft2d</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting exfoliation energies from crystal structure (computed with the OptB88vdW and TBmBJ functionals)</p></td>
<td><p>636</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_log_gvrh</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting DFT log10 VRH-average shear modulus from structure</p></td>
<td><p>10987</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_log_kvrh</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting DFT log10 VRH-average bulk modulus from structure</p></td>
<td><p>10987</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_mp_e_form</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting DFT formation energy from structure</p></td>
<td><p>132752</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_mp_gap</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting DFT PBE band gap from structure</p></td>
<td><p>106113</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_mp_is_metal</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting DFT metallicity from structure</p></td>
<td><p>106113</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_perovskites</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting formation energy from crystal structure</p></td>
<td><p>18928</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_phonons</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting vibration properties from crystal structure</p></td>
<td><p>1265</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">matbench_steels</span></code></p></td>
<td><p>Matbench v0.1 test dataset for predicting steel yield strengths from chemical composition alone</p></td>
<td><p>312</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">mp_all_20181018</span></code></p></td>
<td><p>A complete copy of the Materials Project database as of 10/18/2018</p></td>
<td><p>83989</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">mp_nostruct_20181018</span></code></p></td>
<td><p>A complete copy of the Materials Project database as of 10/18/2018</p></td>
<td><p>83989</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">phonon_dielectric_mp</span></code></p></td>
<td><p>Phonon (lattice/atoms vibrations) and dielectric properties of 1296 compounds computed via ABINIT software package in the harmonic approximation based on density functional perturbation theory.</p></td>
<td><p>1296</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">piezoelectric_tensor</span></code></p></td>
<td><p>941 structures with piezoelectric properties, calculated with DFT-PBE.</p></td>
<td><p>941</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">ricci_boltztrap_mp_tabular</span></code></p></td>
<td><p>Ab-initio electronic transport database for inorganic materials</p></td>
<td><p>47737</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">steel_strength</span></code></p></td>
<td><p>312 steels with experimental yield strength and ultimate tensile strength, extracted and cleaned (including de-duplicating) from Citrine.</p></td>
<td><p>312</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">superconductivity2018</span></code></p></td>
<td><p>Dataset of ~16,000 experimental superconductivity records (critical temperatures) from Stanev et al., originally from the Japanese National Institute for Materials Science</p></td>
<td><p>16414</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">wolverton_oxides</span></code></p></td>
<td><p>4,914 perovskite oxides containing composition data, lattice constants, and formation + vacancy formation energies</p></td>
<td><p>4914</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="dataset-info">
<h1>Dataset info<a class="headerlink" href="#dataset-info" title="Permalink to this headline">¶</a></h1>
<div class="section" id="boltztrap-mp">
<h2>boltztrap_mp<a class="headerlink" href="#boltztrap-mp" title="Permalink to this headline">¶</a></h2>
<p>Effective mass and thermoelectric properties of 8924 compounds in The Materials Project database that are calculated by the BoltzTraP software package run on the GGA-PBE or GGA+U density functional theory calculation results. The properties are reported at the temperature of 300 Kelvin and the carrier concentration of 1e18 1/cm3.</p>
<p><strong>Number of entries:</strong> 8924</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the entry</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">m_n</span></code></p></td>
<td><p>n-type/conduction band effective mass. Units: m_e where m_e is the mass of an electron; i.e. m_n is a unitless ratio</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">m_p</span></code></p></td>
<td><p>p-type/valence band effective mass.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">mpid</span></code></p></td>
<td><p>Materials Project identifier</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">pf_n</span></code></p></td>
<td><p>n-type thermoelectric power factor in uW/cm2.K where uW is microwatts and a constant relaxation time of 1e-14 assumed.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">pf_p</span></code></p></td>
<td><p>p-type power factor in uW/cm2.K</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">s_n</span></code></p></td>
<td><p>n-type Seebeck coefficient in micro Volts per Kelvin</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">s_p</span></code></p></td>
<td><p>p-type Seebeck coefficient in micro Volts per Kelvin</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">structure</span></code></p></td>
<td><p>pymatgen Structure object describing the crystal structure of the material</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Ricci, F. et al. An ab initio electronic transport database for inorganic materials. Sci. Data 4:170085 doi: 10.1038/sdata.2017.85 (2017).
Ricci F, Chen W, Aydemir U, Snyder J, Rignanese G, Jain A, Hautier G (2017) Data from: An ab initio electronic transport database for inorganic materials. Dryad Digital Repository. <a class="reference external" href="https://doi.org/10.5061/dryad.gn001">https://doi.org/10.5061/dryad.gn001</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{Ricci2017, author={Ricci, Francesco and Chen, Wei and Aydemir, Umut and Snyder, G. Jeffrey and Rignanese, Gian-Marco and Jain, Anubhav and Hautier, Geoffroy}, title={An ab initio electronic transport database for inorganic materials}, journal={Scientific Data}, year={2017}, month={Jul}, day={04}, publisher={The Author(s)}, volume={4}, pages={170085}, note={Data Descriptor}, url={http://dx.doi.org/10.1038/sdata.2017.85} }
@misc{dryad_gn001, title = {Data from: An ab initio electronic transport database for inorganic materials}, author = {Ricci, F and Chen, W and Aydemir, U and Snyder, J and Rignanese, G and Jain, A and Hautier, G}, year = {2017}, journal = {Scientific Data}, URL = {https://doi.org/10.5061/dryad.gn001}, doi = {doi:10.5061/dryad.gn001}, publisher = {Dryad Digital Repository} }
</pre></div>
</div>
</div>
<div class="section" id="brgoch-superhard-training">
<h2>brgoch_superhard_training<a class="headerlink" href="#brgoch-superhard-training" title="Permalink to this headline">¶</a></h2>
<p>2574 materials used for training regressors that predict shear and bulk modulus.</p>
<p><strong>Number of entries:</strong> 2574</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">brgoch_feats</span></code></p></td>
<td><p>features used in brgoch study compressed to a dictionary</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">bulk_modulus</span></code></p></td>
<td><p>VRH bulk modulus</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">composition</span></code></p></td>
<td><p>pymatgen composition object</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula as a string</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">material_id</span></code></p></td>
<td><p>materials project id</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">structure</span></code></p></td>
<td><p>pymatgen structure object</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">shear_modulus</span></code></p></td>
<td><p>VRH shear modulus</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">suspect_value</span></code></p></td>
<td><p>True if bulk or shear value did not closely match (within 5%/1GPa of MP) materials project value at time of cross reference or if no material could be found</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Machine Learning Directed Search for Ultraincompressible, Superhard Materials
Aria Mansouri Tehrani, Anton O. Oliynyk, Marcus Parry, Zeshan Rizvi, Samantha Couper, Feng Lin, Lowell Miyagi, Taylor D. Sparks, and Jakoah Brgoch
Journal of the American Chemical Society 2018 140 (31), 9844-9853
DOI: 10.1021/jacs.8b02717</p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{doi:10.1021/jacs.8b02717, author = {Mansouri Tehrani, Aria and Oliynyk, Anton O. and Parry, Marcus and Rizvi, Zeshan and Couper, Samantha and Lin, Feng and Miyagi, Lowell and Sparks, Taylor D. and Brgoch, Jakoah}, title = {Machine Learning Directed Search for Ultraincompressible, Superhard Materials}, journal = {Journal of the American Chemical Society}, volume = {140}, number = {31}, pages = {9844-9853}, year = {2018}, doi = {10.1021/jacs.8b02717}, note ={PMID: 30010335}, URL = { https://doi.org/10.1021/jacs.8b02717 }, eprint = { https://doi.org/10.1021/jacs.8b02717 } }
</pre></div>
</div>
</div>
<div class="section" id="castelli-perovskites">
<h2>castelli_perovskites<a class="headerlink" href="#castelli-perovskites" title="Permalink to this headline">¶</a></h2>
<p>18,928 perovskites generated with ABX combinatorics, calculating gllbsc band gap and pbe structure, and also reporting absolute band edge positions and heat of formation.</p>
<p><strong>Number of entries:</strong> 18928</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">cbm</span></code></p></td>
<td><p>similar to vbm but for conduction band</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">e_form</span></code></p></td>
<td><p>heat of formation in eV, Note the reference state for oxygen was computed from oxygen’s chemical potential in water vapor, not as oxygen molecules, to reflect the application which these perovskites were studied for.</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">fermi</span> <span class="pre">level</span></code></p></td>
<td><p>the thermodynamic work required to add one electron to the body in eV</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">fermi</span> <span class="pre">width</span></code></p></td>
<td><p>fermi bandwidth</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">gap</span> <span class="pre">gllbsc</span></code></p></td>
<td><p>electronic band gap in eV calculated via gllbsc functional</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">gap</span> <span class="pre">is</span> <span class="pre">direct</span></code></p></td>
<td><p>boolean indicator for direct gap</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">mu_b</span></code></p></td>
<td><p>magnetic moment in terms of Bohr magneton</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">structure</span></code></p></td>
<td><p>crystal structure represented by pymatgen Structure object</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">vbm</span></code></p></td>
<td><p>absolute value of valence band edge calculated via gllbsc</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Ivano E. Castelli, David D. Landis, Kristian S. Thygesen, Søren Dahl, Ib Chorkendorff, Thomas F. Jaramillo and Karsten W. Jacobsen (2012) New cubic perovskites for one- and two-photon water splitting using the computational materials repository. Energy Environ. Sci., 2012,5, 9034-9043 <a class="reference external" href="https://doi.org/10.1039/C2EE22341D">https://doi.org/10.1039/C2EE22341D</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{C2EE22341D, author ="Castelli, Ivano E. and Landis, David D. and Thygesen, Kristian S. and Dahl, Søren and Chorkendorff, Ib and Jaramillo, Thomas F. and Jacobsen, Karsten W.", title ="New cubic perovskites for one- and two-photon water splitting using the computational materials repository", journal ="Energy Environ. Sci.", year ="2012", volume ="5", issue ="10", pages ="9034-9043", publisher ="The Royal Society of Chemistry", doi ="10.1039/C2EE22341D", url ="http://dx.doi.org/10.1039/C2EE22341D", abstract ="A new efficient photoelectrochemical cell (PEC) is one of the possible solutions to the energy and climate problems of our time. Such a device requires development of new semiconducting materials with tailored properties with respect to stability and light absorption. Here we perform computational screening of around 19 000 oxides{,} oxynitrides{,} oxysulfides{,} oxyfluorides{,} and oxyfluoronitrides in the cubic perovskite structure with PEC applications in mind. We address three main applications: light absorbers for one- and two-photon water splitting and high-stability transparent shields to protect against corrosion. We end up with 20{,} 12{,} and 15 different combinations of oxides{,} oxynitrides and oxyfluorides{,} respectively{,} inviting further experimental investigation."}
</pre></div>
</div>
</div>
<div class="section" id="citrine-thermal-conductivity">
<h2>citrine_thermal_conductivity<a class="headerlink" href="#citrine-thermal-conductivity" title="Permalink to this headline">¶</a></h2>
<p>Thermal conductivity of 872 compounds measured experimentally and retrieved from Citrine database from various references. The reported values are measured at various temperatures of which 295 are at room temperature.</p>
<p><strong>Number of entries:</strong> 872</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the dataset entry</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">k-units</span></code></p></td>
<td><p>units of thermal conductivity</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">k_condition</span></code></p></td>
<td><p>Temperature description of testing conditions</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">k_condition_units</span></code></p></td>
<td><p>units of testing condition temperature representation</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">k_expt</span></code></p></td>
<td><p>the experimentally measured thermal conductivity in SI units of W/m.K</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p><a class="reference external" href="https://www.citrination.com">https://www.citrination.com</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@misc{Citrine Informatics, title = {Citrination}, howpublished = {\url{https://www.citrination.com/}}, }
</pre></div>
</div>
</div>
<div class="section" id="dielectric-constant">
<h2>dielectric_constant<a class="headerlink" href="#dielectric-constant" title="Permalink to this headline">¶</a></h2>
<p>1,056 structures with dielectric properties, calculated with DFPT-PBE.</p>
<p><strong>Number of entries:</strong> 1056</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">band_gap</span></code></p></td>
<td><p>Measure of the conductivity of a material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">cif</span></code></p></td>
<td><p>optional: Description string for structure</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">e_electronic</span></code></p></td>
<td><p>electronic contribution to dielectric tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">e_total</span></code></p></td>
<td><p>Total dielectric tensor incorporating both electronic and ionic contributions</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">material_id</span></code></p></td>
<td><p>Materials Project ID of the material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">meta</span></code></p></td>
<td><p>optional, metadata descriptor of the datapoint</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">n</span></code></p></td>
<td><p>Refractive Index</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">nsites</span></code></p></td>
<td><p>The # of atoms in the unit cell of the calculation.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">poly_electronic</span></code></p></td>
<td><p>the average of the eigenvalues of the electronic contribution to the dielectric tensor</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">poly_total</span></code></p></td>
<td><p>the average of the eigenvalues of the total (electronic and ionic) contributions to the dielectric tensor</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">poscar</span></code></p></td>
<td><p>optional: Poscar metadata</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">pot_ferroelectric</span></code></p></td>
<td><p>Whether the material is potentially ferroelectric</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">space_group</span></code></p></td>
<td><p>Integer specifying the crystallographic structure of the material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">structure</span></code></p></td>
<td><p>pandas Series defining the structure of the material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">volume</span></code></p></td>
<td><p>Volume of the unit cell in cubic angstroms, For supercell calculations, this quantity refers to the volume of the full supercell.</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Petousis, I., Mrdjenovich, D., Ballouz, E., Liu, M., Winston, D.,
Chen, W., Graf, T., Schladt, T. D., Persson, K. A. & Prinz, F. B.
High-throughput screening of inorganic compounds for the discovery
of novel dielectric and optical materials. Sci. Data 4, 160134 (2017).</p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{Petousis2017, author={Petousis, Ioannis and Mrdjenovich, David and Ballouz, Eric and Liu, Miao and Winston, Donald and Chen, Wei and Graf, Tanja and Schladt, Thomas D. and Persson, Kristin A. and Prinz, Fritz B.}, title={High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials}, journal={Scientific Data}, year={2017}, month={Jan}, day={31}, publisher={The Author(s)}, volume={4}, pages={160134}, note={Data Descriptor}, url={http://dx.doi.org/10.1038/sdata.2016.134} }
</pre></div>
</div>
</div>
<div class="section" id="double-perovskites-gap">
<h2>double_perovskites_gap<a class="headerlink" href="#double-perovskites-gap" title="Permalink to this headline">¶</a></h2>
<p>Band gap of 1306 double perovskites (a_1-b_1-a_2-b_2-O6) calculated using Gritsenko, van Leeuwen, van Lenthe and Baerends potential (gllbsc) in GPAW.</p>
<p><strong>Number of entries:</strong> 1306</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">a_1</span></code></p></td>
<td><p>Species occupying the a1 perovskite site</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">a_2</span></code></p></td>
<td><p>Species occupying the a2 site</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">b_1</span></code></p></td>
<td><p>Species occupying the b1 site</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">b_2</span></code></p></td>
<td><p>Species occupying the b2 site</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the entry</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">gap</span> <span class="pre">gllbsc</span></code></p></td>
<td><p>electronic band gap (in eV) calculated via gllbsc</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Dataset discussed in:
Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375; doi: 10.1038/srep19375 (2016).
Dataset sourced from:
<a class="reference external" href="https://cmr.fysik.dtu.dk/">https://cmr.fysik.dtu.dk/</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{Pilania2016, author={Pilania, G. and Mannodi-Kanakkithodi, A. and Uberuaga, B. P. and Ramprasad, R. and Gubernatis, J. E. and Lookman, T.}, title={Machine learning bandgaps of double perovskites}, journal={Scientific Reports}, year={2016}, month={Jan}, day={19}, publisher={The Author(s)}, volume={6}, pages={19375}, note={Article}, url={http://dx.doi.org/10.1038/srep19375} }
@misc{Computational Materials Repository, title = {Computational Materials Repository}, howpublished = {\url{https://cmr.fysik.dtu.dk/}}, }
</pre></div>
</div>
</div>
<div class="section" id="double-perovskites-gap-lumo">
<h2>double_perovskites_gap_lumo<a class="headerlink" href="#double-perovskites-gap-lumo" title="Permalink to this headline">¶</a></h2>
<p>Supplementary lumo data of 55 atoms for the double_perovskites_gap dataset.</p>
<p><strong>Number of entries:</strong> 55</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">atom</span></code></p></td>
<td><p>Name of the atom whos lumo is listed</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">lumo</span></code></p></td>
<td><p>Lowest unoccupied molecular obital energy level (in eV)</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Dataset discussed in:
Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375; doi: 10.1038/srep19375 (2016).
Dataset sourced from:
<a class="reference external" href="https://cmr.fysik.dtu.dk/">https://cmr.fysik.dtu.dk/</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{Pilania2016, author={Pilania, G. and Mannodi-Kanakkithodi, A. and Uberuaga, B. P. and Ramprasad, R. and Gubernatis, J. E. and Lookman, T.}, title={Machine learning bandgaps of double perovskites}, journal={Scientific Reports}, year={2016}, month={Jan}, day={19}, publisher={The Author(s)}, volume={6}, pages={19375}, note={Article}, url={http://dx.doi.org/10.1038/srep19375} }
@misc{Computational Materials Repository, title = {Computational Materials Repository}, howpublished = {\url{https://cmr.fysik.dtu.dk/}}, }
</pre></div>
</div>
</div>
<div class="section" id="elastic-tensor-2015">
<h2>elastic_tensor_2015<a class="headerlink" href="#elastic-tensor-2015" title="Permalink to this headline">¶</a></h2>
<p>1,181 structures with elastic properties calculated with DFT-PBE.</p>
<p><strong>Number of entries:</strong> 1181</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">G_Reuss</span></code></p></td>
<td><p>Lower bound on shear modulus for polycrystalline material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">G_VRH</span></code></p></td>
<td><p>Average of G_Reuss and G_Voigt</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">G_Voigt</span></code></p></td>
<td><p>Upper bound on shear modulus for polycrystalline material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">K_Reuss</span></code></p></td>
<td><p>Lower bound on bulk modulus for polycrystalline material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">K_VRH</span></code></p></td>
<td><p>Average of K_Reuss and K_Voigt</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">K_Voigt</span></code></p></td>
<td><p>Upper bound on bulk modulus for polycrystalline material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">cif</span></code></p></td>
<td><p>optional: Description string for structure</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">compliance_tensor</span></code></p></td>
<td><p>Tensor describing elastic behavior</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">elastic_anisotropy</span></code></p></td>
<td><p>measure of directional dependence of the materials elasticity, metric is always >= 0</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">elastic_tensor</span></code></p></td>
<td><p>Tensor describing elastic behavior corresponding to IEEE orientation, symmetrized to crystal structure</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">elastic_tensor_original</span></code></p></td>
<td><p>Tensor describing elastic behavior, unsymmetrized, corresponding to POSCAR conventional standard cell orientation</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">kpoint_density</span></code></p></td>
<td><p>optional: Sampling parameter from calculation</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">material_id</span></code></p></td>
<td><p>Materials Project ID of the material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">nsites</span></code></p></td>
<td><p>The # of atoms in the unit cell of the calculation.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">poisson_ratio</span></code></p></td>
<td><p>Describes lateral response to loading</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">poscar</span></code></p></td>
<td><p>optional: Poscar metadata</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">space_group</span></code></p></td>
<td><p>Integer specifying the crystallographic structure of the material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">structure</span></code></p></td>
<td><p>pandas Series defining the structure of the material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">volume</span></code></p></td>
<td><p>Volume of the unit cell in cubic angstroms, For supercell calculations, this quantity refers to the volume of the full supercell.</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Jong, M. De, Chen, W., Angsten, T., Jain, A., Notestine, R., Gamst,
A., Sluiter, M., Ande, C. K., Zwaag, S. Van Der, Plata, J. J., Toher,
C., Curtarolo, S., Ceder, G., Persson, K. and Asta, M., “Charting
the complete elastic properties of inorganic crystalline compounds”,
Scientific Data volume 2, Article number: 150009 (2015)</p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{deJong2015, author={de Jong, Maarten and Chen, Wei and Angsten, Thomas and Jain, Anubhav and Notestine, Randy and Gamst, Anthony and Sluiter, Marcel and Krishna Ande, Chaitanya and van der Zwaag, Sybrand and Plata, Jose J. and Toher, Cormac and Curtarolo, Stefano and Ceder, Gerbrand and Persson, Kristin A. and Asta, Mark}, title={Charting the complete elastic properties of inorganic crystalline compounds}, journal={Scientific Data}, year={2015}, month={Mar}, day={17}, publisher={The Author(s)}, volume={2}, pages={150009}, note={Data Descriptor}, url={http://dx.doi.org/10.1038/sdata.2015.9} }
</pre></div>
</div>
</div>
<div class="section" id="expt-formation-enthalpy">
<h2>expt_formation_enthalpy<a class="headerlink" href="#expt-formation-enthalpy" title="Permalink to this headline">¶</a></h2>
<p>Experimental formation enthalpies for inorganic compounds, collected from years of calorimetric experiments. There are 1,276 entries in this dataset, mostly binary compounds. Matching mpids or oqmdids as well as the DFT-computed formation energies are also added (if any).</p>
<p><strong>Number of entries:</strong> 1276</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">e_form</span> <span class="pre">expt</span></code></p></td>
<td><p>experimental formation enthalpy (in eV/atom)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">e_form</span> <span class="pre">mp</span></code></p></td>
<td><p>formation enthalpy from Materials Project (in eV/atom)</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">e_form</span> <span class="pre">oqmd</span></code></p></td>
<td><p>formation enthalpy from OQMD (in eV/atom)</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>chemical formula</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">mpid</span></code></p></td>
<td><p>materials project id</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">oqmdid</span></code></p></td>
<td><p>OQMD id</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">pearson</span> <span class="pre">symbol</span></code></p></td>
<td><p>pearson symbol of the structure</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">space</span> <span class="pre">group</span></code></p></td>
<td><p>space group of the structure</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p><a class="reference external" href="https://www.nature.com/articles/sdata2017162">https://www.nature.com/articles/sdata2017162</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{Kim2017, author={Kim, George and Meschel, S. V. and Nash, Philip and Chen, Wei}, title={Experimental formation enthalpies for intermetallic phases and other inorganic compounds}, journal={Scientific Data}, year={2017}, month={Oct}, day={24}, publisher={The Author(s)}, volume={4}, pages={170162}, note={Data Descriptor}, url={https://doi.org/10.1038/sdata.2017.162}}
@misc{kim_meschel_nash_chen_2017, title={Experimental formation enthalpies for intermetallic phases and other inorganic compounds}, url={https://figshare.com/collections/Experimental_formation_enthalpies_for_intermetallic_phases_and_other_inorganic_compounds/3822835/1}, DOI={10.6084/m9.figshare.c.3822835.v1}, abstractNote={The standard enthalpy of formation of a compound is the energy associated with the reaction to form the compound from its component elements. The standard enthalpy of formation is a fundamental thermodynamic property that determines its phase stability, which can be coupled with other thermodynamic data to calculate phase diagrams. Calorimetry provides the only direct method by which the standard enthalpy of formation is experimentally measured. However, the measurement is often a time and energy intensive process. We present a dataset of enthalpies of formation measured by high-temperature calorimetry. The phases measured in this dataset include intermetallic compounds with transition metal and rare-earth elements, metal borides, metal carbides, and metallic silicides. These measurements were collected from over 50 years of calorimetric experiments. The dataset contains 1,276 entries on experimental enthalpy of formation values and structural information. Most of the entries are for binary compounds but ternary and quaternary compounds are being added as they become available. The dataset also contains predictions of enthalpy of formation from first-principles calculations for comparison.}, publisher={figshare}, author={Kim, George and Meschel, Susan and Nash, Philip and Chen, Wei}, year={2017}, month={Oct}}
</pre></div>
</div>
</div>
<div class="section" id="expt-formation-enthalpy-kingsbury">
<h2>expt_formation_enthalpy_kingsbury<a class="headerlink" href="#expt-formation-enthalpy-kingsbury" title="Permalink to this headline">¶</a></h2>
<p>Dataset containing experimental standard formation enthalpies for solids. Formation enthalpies were compiled primarily from Kim et al., Kubaschewski, and the NIST JANAF tables (see references). Elements, liquids, and gases were excluded. Data were deduplicated such that each material is associated with a single formation enthalpy value. Refer to Wang et al. (see references) for a complete desciption of the methods used. Materials Project database IDs (mp-ids) were assigned to materials from among computed materials in the Materials Project database (version 2021.03.22) that were 1) not marked ‘theoretical’, 2) had structures matching at least one ICSD material, and 3) were within 200 meV of the DFT-computed stable energy hull (e_above_hull < 0.2 eV). Among these candidates, we chose the mp-id with the lowest e_above_hull that matched the reported spacegroup (where available).</p>
<p><strong>Number of entries:</strong> 2135</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">expt_form_e</span></code></p></td>
<td><p>Experimental standard formation enthalpy (298 K), in eV/atom.</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">uncertainty</span></code></p></td>
<td><p>Uncertainty reported in the experimental formation energy, in eV/atom.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">phaseinfo</span></code></p></td>
<td><p>Description of the material’s crystal structure or space group.</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">reference</span></code></p></td>
<td><p>Reference to the original data source.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">likely_mpid</span></code></p></td>
<td><p>Materials Project database ID (mp-id) most likely associated with each material.</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Wang, A., Kingsbury, R., McDermott, M., Horton, M., Jain. A., Ong, S.P., Dwaraknath, S., Persson, K. A framework for quantifying uncertainty in DFT energy corrections. ChemRxiv. Preprint. <a class="reference external" href="https://doi.org/10.26434/chemrxiv.14593476.v1">https://doi.org/10.26434/chemrxiv.14593476.v1</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{Kim2017,doi={10.1038/sdata.2017.162},url={https://doi.org/10.1038/sdata.2017.162},year={2017},month=oct,publisher={Springer Science and Business Media {LLC}}, volume = {4}, number = {1}, author = {George Kim and S. V. Meschel and Philip Nash and Wei Chen},title ={Experimental formation enthalpies for intermetallic phases and other inorganic compounds},journal={Scientific Data}}
@misc{kim_meschel_nash_chen_2017, title={Experimental formation enthalpies for intermetallic phases and other inorganic compounds}, url={https://springernature.figshare.com/collections/Experimental_formation_enthalpies_for_intermetallic_phases_and_other_inorganic_compounds/3822835/1}, DOI={10.6084/m9.figshare.c.3822835.v1}, publisher={figshare},author={Kim, George and Meschel, Susan and Nash, Philip and Chen, Wei}, year={2017}, month={Oct} }
@article{Kim2017, doi = {10.1038/sdata.2017.162}, url = {https://doi.org/10.1038/sdata.2017.162}, year = {2017}, month = oct, publisher = {Springer Science and Business Media LLC}}, volume = {4}, number = {1},author = {George Kim and S. V. Meschel and Philip Nash and Wei Chen},title = {Experimental formation enthalpies for intermetallic phases and other inorganic compounds},journal = {Scientific Data}}
@book{Kubaschewski1993,author={Kubaschewski, O. and Alcock, C.B. and Spencer, P.J.},edition={6th},isbn={0080418880},publisher={Pergamon Press},title={{Materials Thermochemistry}},year = {1993}}
@misc{NIST,doi = {10.18434/T42S31},url = {http://kinetics.nist.gov/janaf/},author = {Malcolm W. Chase}, title = {NIST-JANAF Thermochemical Tables}, publisher = {National Institute of Standards and Technology}, year = {1998}, url={https://janaf.nist.org}}
@article{RZYMAN2000309,title = {Enthalpies of formation of AlFe: Experiment versus theory},journal = {Calphad},volume = {24},number = {3},pages = {309-318},year = {2000}, issn = {0364-5916},doi = {https://doi.org/10.1016/S0364-5916(01)00007-4}, url = {https://www.sciencedirect.com/science/article/pii/S0364591601000074}, author = {K. Rzyman and Z. Moser and A.P. Miodownik and L. Kaufman and R.E. Watson and M. Weinert}}
@book{CRC2007,asin = {0849304881},author = {{CRC Handbook}},dewey = {530},ean = {9780849304880},edition = 88,interhash = {da6394e1a9c5f450ed705c32ec82bb08},intrahash = {5ff8f541915536461697300e8727f265},isbn = {0849304881},keywords = {crc_handbook},publisher = {CRC Press},title = {CRC Handbook of Chemistry and Physics, 88th Edition}, year = 2007}
@article{Grindy2013,author = {Grindy, Scott and Meredig, Bryce and Kirklin, Scott and Saal, James E. and Wolverton, C.},doi = {10.1103/PhysRevB.87.075150},issn = {10980121},journal = {Physical Review B - Condensed Matter and Materials Physics},number = {7},pages = {1--8},title = {{Approaching chemical accuracy with density functional calculations: Diatomic energy corrections}},volume = {87},year = {2013}}
</pre></div>
</div>
</div>
<div class="section" id="expt-gap">
<h2>expt_gap<a class="headerlink" href="#expt-gap" title="Permalink to this headline">¶</a></h2>
<p>Experimental band gap of 6354 inorganic semiconductors.</p>
<p><strong>Number of entries:</strong> 6354</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>chemical formula</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">gap</span> <span class="pre">expt</span></code></p></td>
<td><p>band gap (in eV) measured experimentally</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p><a class="reference external" href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpclett.8b00124">https://pubs.acs.org/doi/suppl/10.1021/acs.jpclett.8b00124</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{doi:10.1021/acs.jpclett.8b00124, author = {Zhuo, Ya and Mansouri Tehrani, Aria and Brgoch, Jakoah}, title = {Predicting the Band Gaps of Inorganic Solids by Machine Learning}, journal = {The Journal of Physical Chemistry Letters}, volume = {9}, number = {7}, pages = {1668-1673}, year = {2018}, doi = {10.1021/acs.jpclett.8b00124}, note ={PMID: 29532658}, eprint = { https://doi.org/10.1021/acs.jpclett.8b00124 }}
</pre></div>
</div>
</div>
<div class="section" id="expt-gap-kingsbury">
<h2>expt_gap_kingsbury<a class="headerlink" href="#expt-gap-kingsbury" title="Permalink to this headline">¶</a></h2>
<p>Identical to the matbench_expt_gap dataset, except that Materials Project database IDs (mp-ids) have been associated with each material using the same method as described for the expt_formation_enthalpy_kingsbury dataset. Columns have also been renamed for consistency with the formation enthalpy data.</p>
<p><strong>Number of entries:</strong> 4604</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">expt_gap</span></code></p></td>
<td><p>Experimentally measured bandgap, in eV.</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">likely_mpid</span></code></p></td>
<td><p>Materials Project database ID (mp-id) most likely associated with each material.</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Kingsbury, R., Bartel., C., Dwaraknath, S., Gupta, A., Horton, M., Munro, J., Jain. A., Ong, S.P., Persson, K. Comparison of r$^2$SCAN and SCAN metaGGA functionals via an automated, high-throughput computational workflow. In preparation.</p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@Article{Dunn2020, author={Dunn, Alexander and Wang, Qi and Ganose, Alex and Dopp, Daniel and Jain, Anubhav}, title={Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm}, journal={npj Computational Materials}, year={2020}, month={Sep}, day={15}, volume={6}, number={1}, pages={138}, abstract={We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13{\thinspace}ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material's composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm---namely, that crystal graph methods appear to outperform traditional machine learning methods given {\textasciitilde}104 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.}, issn={2057-3960}, doi={10.1038/s41524-020-00406-3}, url={https://doi.org/10.1038/s41524-020-00406-3} }
@article{doi:10.1021/acs.jpclett.8b00124, author = {Zhuo, Ya and Mansouri Tehrani, Aria and Brgoch, Jakoah}, title = {Predicting the Band Gaps of Inorganic Solids by Machine Learning}, journal = {The Journal of Physical Chemistry Letters}, volume = {9}, number = {7}, pages = {1668-1673}, year = {2018}, doi = {10.1021/acs.jpclett.8b00124}, note ={PMID: 29532658}, eprint = { https://doi.org/10.1021/acs.jpclett.8b00124 }}
</pre></div>
</div>
</div>
<div class="section" id="flla">
<h2>flla<a class="headerlink" href="#flla" title="Permalink to this headline">¶</a></h2>
<p>3938 structures and computed formation energies from “Crystal Structure Representations for Machine Learning Models of Formation Energies.”</p>
<p><strong>Number of entries:</strong> 3938</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">e_above_hull</span></code></p></td>
<td><p>The energy of decomposition of this material into the set of most stable materials at this chemical composition, in eV/atom.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">formation_energy</span></code></p></td>
<td><p>Computed formation energy at 0K, 0atm using a reference state of zero for the pure elements.</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formation_energy_per_atom</span></code></p></td>
<td><p>See formation_energy</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the material</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">material_id</span></code></p></td>
<td><p>Materials Project ID of the material</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">nsites</span></code></p></td>
<td><p>The # of atoms in the unit cell of the calculation.</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">structure</span></code></p></td>
<td><p>pandas Series defining the structure of the material</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>1) F. Faber, A. Lindmaa, O.A. von Lilienfeld, R. Armiento,
“Crystal structure representations for machine learning models of
formation energies”, Int. J. Quantum Chem. 115 (2015) 1094–1101.
doi:10.1002/qua.24917.</p>
<p>(raw data)
2) Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D.,
Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G. & Persson,
K. A. Commentary: The Materials Project: A materials genome approach
to accelerating materials innovation. APL Mater. 1, 11002 (2013).</p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{doi:10.1002/qua.24917, author = {Faber, Felix and Lindmaa, Alexander and von Lilienfeld, O. Anatole and Armiento, Rickard}, title = {Crystal structure representations for machine learning models of formation energies}, journal = {International Journal of Quantum Chemistry}, volume = {115}, number = {16}, pages = {1094-1101}, keywords = {machine learning, formation energies, representations, crystal structure, periodic systems}, doi = {10.1002/qua.24917}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/qua.24917}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/qua.24917}, abstract = {We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a dataset of 3938 crystal structures obtained from the Materials Project. For training sets consisting of 3000 crystals, the generalization error in predicting formation energies of new structures corresponds to (i) 0.49, (ii) 0.64, and (iii) for the respective representations. © 2015 Wiley Periodicals, Inc.} }
@article{doi:10.1063/1.4812323, author = {Jain,Anubhav and Ong,Shyue Ping and Hautier,Geoffroy and Chen,Wei and Richards,William Davidson and Dacek,Stephen and Cholia,Shreyas and Gunter,Dan and Skinner,David and Ceder,Gerbrand and Persson,Kristin A. }, title = {Commentary: The Materials Project: A materials genome approach to accelerating materials innovation}, journal = {APL Materials}, volume = {1}, number = {1}, pages = {011002}, year = {2013}, doi = {10.1063/1.4812323}, URL = {https://doi.org/10.1063/1.4812323}, eprint = {https://doi.org/10.1063/1.4812323} }
</pre></div>
</div>
</div>
<div class="section" id="glass-binary">
<h2>glass_binary<a class="headerlink" href="#glass-binary" title="Permalink to this headline">¶</a></h2>
<p>Metallic glass formation data for binary alloys, collected from various experimental techniques such as melt-spinning or mechanical alloying. This dataset covers all compositions with an interval of 5 at. % in 59 binary systems, containing a total of 5959 alloys in the dataset. The target property of this dataset is the glass forming ability (GFA), i.e. whether the composition can form monolithic glass or not, which is either 1 for glass forming or 0 for non-full glass forming.</p>
<p><strong>Number of entries:</strong> 5959</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>chemical formula</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">gfa</span></code></p></td>
<td><p>glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming (“AM”), 0: non-full-forming(“CR”)</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p><a class="reference external" href="https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046">https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{doi:10.1021/acs.jpclett.7b01046, author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.}, title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability}, journal = {The Journal of Physical Chemistry Letters}, volume = {8}, number = {14}, pages = {3434-3439}, year = {2017}, doi = {10.1021/acs.jpclett.7b01046}, note ={PMID: 28697303}, eprint = { https://doi.org/10.1021/acs.jpclett.7b01046 }}
</pre></div>
</div>
</div>
<div class="section" id="glass-binary-v2">
<h2>glass_binary_v2<a class="headerlink" href="#glass-binary-v2" title="Permalink to this headline">¶</a></h2>
<p>Identical to glass_binary dataset, but with duplicate entries merged. If there was a disagreement in gfa when merging the class was defaulted to 1.</p>
<p><strong>Number of entries:</strong> 5483</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>chemical formula</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">gfa</span></code></p></td>
<td><p>glass forming ability, correlated with the phase column, designating whether the composition can form monolithic glass or not, 1: glass forming (“AM”), 0: non-full-forming(“CR”)</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p><a class="reference external" href="https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046">https://pubs.acs.org/doi/10.1021/acs.jpclett.7b01046</a></p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article{doi:10.1021/acs.jpclett.7b01046, author = {Sun, Y. T. and Bai, H. Y. and Li, M. Z. and Wang, W. H.}, title = {Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability}, journal = {The Journal of Physical Chemistry Letters}, volume = {8}, number = {14}, pages = {3434-3439}, year = {2017}, doi = {10.1021/acs.jpclett.7b01046}, note ={PMID: 28697303}, eprint = { https://doi.org/10.1021/acs.jpclett.7b01046 }}
</pre></div>
</div>
</div>
<div class="section" id="glass-ternary-hipt">
<h2>glass_ternary_hipt<a class="headerlink" href="#glass-ternary-hipt" title="Permalink to this headline">¶</a></h2>
<p>Metallic glass formation dataset for ternary alloys, collected from the high-throughput sputtering experiments measuring whether it is possible to form a glass using sputtering. The hipt experimental data are of the Co-Fe-Zr, Co-Ti-Zr, Co-V-Zr and Fe-Ti-Nb ternary systems.</p>
<p><strong>Number of entries:</strong> 5170</p>
<table class="colwidths-given docutils align-left">
<colgroup>
<col style="width: 20%" />
<col style="width: 80%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"><p>Column</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">formula</span></code></p></td>
<td><p>Chemical formula of the entry</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">gfa</span></code></p></td>
<td><p>Glass forming ability: 1 means glass forming and coresponds to AM, 0 means non glass forming and corresponds to CR</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">phase</span></code></p></td>
<td><p>AM: amorphous phase or CR: crystalline phase</p></td>
</tr>
<tr class="row-odd"><td><p><code class="code docutils literal notranslate"><span class="pre">processing</span></code></p></td>
<td><p>How the point was processed, always sputtering for this dataset</p></td>
</tr>
<tr class="row-even"><td><p><code class="code docutils literal notranslate"><span class="pre">system</span></code></p></td>
<td><p>System of dataset experiment, one of: CoFeZr, CoTiZr, CoVZr, or FeTiNb</p></td>
</tr>
</tbody>
</table>
<p><strong>Reference</strong></p>
<p>Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
By Fang Ren, Logan Ward, Travis Williams, Kevin J. Laws, Christopher Wolverton, Jason Hattrick-Simpers, Apurva Mehta
Science Advances 13 Apr 2018 : eaaq1566</p>
<p><strong>Bibtex Formatted Citations</strong></p>
<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>@article {Reneaaq1566, author = {Ren, Fang and Ward, Logan and Williams, Travis and Laws, Kevin J. and Wolverton, Christopher and Hattrick-Simpers, Jason and Mehta, Apurva}, title = {Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments}, volume = {4}, number = {4}, year = {2018}, doi = {10.1126/sciadv.aaq1566}, publisher = {American Association for the Advancement of Science}, abstract = {With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method{\textendash}dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method{\textendash}sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path{\textendash}dependent and that current physiochemical theories find challenging to predict.}, URL = {http://advances.sciencemag.org/content/4/4/eaaq1566}, eprint = {http://advances.sciencemag.org/content/4/4/eaaq1566.full.pdf}, journal = {Science Advances} }