The materials for the Spring Mathematics in Materials course at the UTK MSE

Yet another ML method comparison

Molecular Generation by Fast Assembly of SMILES Fragments

Easy to use docking tools based on autodock Vina

A repository for evaluating single-step retrosynthesis algorithms

Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates (PWAS/PWASp)

Code for "Molecular Hypergraph Grammar with Its Application to Molecular Optimization"

Reimplementation of Automatic Chemical Design Using a Data-DrivenContinuous Representation of Molecules (https://arxiv.org/pdf/1610.02415.pdf) on MNIST in JAX

quacc is a flexible platform for computational materials science and quantum chemistry that is built for the big data era.

List of molecular design using Generative AI and Deep Learning

A convolutional neural network that identifies water in satellite images.

Code for paper "Human-in-the-Loop Assisted de Novo Molecular Design".

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

A Quantum Computing library that reconstructs quantum states using 3 stacked GRU Cells.

Datasets and codes for the paper: "Chemical Hardness-Driven Interpretable Machine Learning Approach for Rapid Search of Photocatalysts"

Resource, Evaluation and Detection Papers for ChatGPT

Bayesian optimisation & Reinforcement Learning library developped by Huawei Noah's Ark Lab

Experimental design and (multi-objective) bayesian optimization.

This repo contains the datasets used for the paper The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.

add-on to plotly which show molecule images on mouseover!

Matplotlib style sheets to nicely format figures for scientific papers, thesis and presentations while keeping them fully editable in Adobe Illustrator.

Isotropic Gaussian Processs on Finite Spaces of Graphs (AISTATS 2023)

Code for the NeurIPS 2020 paper: "Federated Bayesian Optimization via Thompson Sampling"

Bayesian Optimization Meets Bayesian Optimal Stopping

Readings on computational logic, interactive theorem proving and functional programming.

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks