AI-enhanced computational chemistry
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Updated
May 15, 2024 - Python
AI-enhanced computational chemistry
Machine Learning Code Implementations in Python
PERK: Parameter Estimation via Regression with Kernels
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.
MLQD is a Python Package for Machine Learning-based Quantum Dissipative Dynamics
SM4ML project
pwtools is a Python package for pre- and postprocessing of atomistic calculations, mostly targeted to Quantum Espresso, CPMD, CP2K and LAMMPS. It is almost, but not quite, entirely unlike ASE, with some tools extending numpy/scipy. It has a set of powerful parsers and data types for storing calculation data.
Explore selected topics related to Gaussian processes
This repository contains code for predicting stock prices using various machine learning models. The models implemented include Linear Regression, SVM Regression, KNN Regression, Kernel Ridge Regression, and Ridge Regression.
Lecture "Softwareprojekt" @fh-wedel WS20
Machine learning regression model to predict energy consumption and GHG emission
House Prices - Advanced Regression Techniques
Anticipate the energy consumption of new commercial buildings
Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668
Lecture "Learning & soft computing" @fh-wedel SS22
Kernel-Methods on a Red-Wine Dataset
Amons-based quantum machine learning for quantum chemistry
2017 Summer School on the Machine Learning in the Molecular Sciences. This project aims to help you understand some basic machine learning models including neural network optimization plan, random forest, parameter learning, incremental learning paradigm, clustering and decision tree, etc. based on kernel regression and dimensionality reduction,…
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