AI-enhanced computational chemistry

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.

Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

Amons-based quantum machine learning for quantum chemistry

Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression

MLQD is a Python Package for Machine Learning-based Quantum Dissipative Dynamics

Machine learning (linear regression & kernel-ridge regression) examples on the Boston housing dataset

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,…

Machine Learning Code Implementations in Python

Speeding up quantum dissipative dynamics of open systems with kernel methods

Codes and experiments for paper "Distributed Learning with Random Features". Preprint.

Contains ML Algorithms implemented as part of CSE 512 - Machine Learning class taken by Fransico Orabona. Implemented Linear Regression using polynomial basis functions, Perceptron, Ridge Regression, SVM Primal, Kernel Ridge Regression, Kernel SVM, Kmeans.

kernel linear regression and svm for Creditcard and Tumor data

Explore selected topics related to Gaussian processes

Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668

Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.

Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning

Lecture "Learning & soft computing" @fh-wedel SS22

Repository associated with article "A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors"