AI-enhanced computational chemistry
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Updated
Nov 21, 2024 - Python
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.
Amons-based quantum machine learning for quantum chemistry
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
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
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.
Codes and experiments for paper "Distributed Learning with Random Features". Preprint.
Speeding up quantum dissipative dynamics of open systems with kernel methods
kernel linear regression and svm for Creditcard and Tumor data
Pytorch implementation of Alchemical Kernels from Phys. Chem. Chem. Phys., 2018,20, 29661-29668
Lecture "Learning & soft computing" @fh-wedel SS22
Explore selected topics related to Gaussian processes
Sequential Regression Extrapolation (SRE): An accurate method of extrapolation using machine learning
Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.
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