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Next-gen fast plotting library running on WGPU using the pygfx rendering engine
FORGE (Flexible Optimizer for Rapid Generation and Exploration) to guide machine learning interatomic potential development.
Grand canonical optimization of grain boundary phases.
DeePMD-kit plugin for various graph neural network models
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
List of Molecular and Material design using Generative AI and Deep Learning
Implementations of different GNNs from scratch for chemists
existing state-of-the-art GNN models for energy and force prediction tasks, combined with MD calculator through LAMMPS
Atomistic machine learning models you can use everywhere for everything
A high-level C++ interface to the Message Passing Interface
Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".
A Python package for enhancing VASP AIMD simulations and analysis
Reproduction of CGCNN with fine-tuning for predicting material properties
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Training Neural Network potentials through customizable routines in JAX.
computational physics class taught at UNLV (Phys300)