PSP is a python toolkit for predicting atomic-level structural models for a range of polymer geometries.

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

SECS is a machine learning framework designed for structure elucidation from spectra such as NMR and IR.

All graph/GNN papers accepted at NeurIPS 2024.

NMR structure elucidation

an efficient distributed PyTorch framework

Code for generation and benchmarks of the Multimodal Spectroscopic Dataset

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

Library augmented Symbolic Regression in Julia

gpt-2 from scratch in mlx

MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.

bert-loves-chemistry: a repository of HuggingFace models applied on chemical SMILES data for drug design, chemical modelling, etc.

[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, D…

Python PDF parser for scientific publications: content and figures

Distributed High-Performance Symbolic Regression in Julia