NequIP is a code for building E(3)-equivariant interatomic potentials
- Updated
Nov 5, 2025 - Python
NequIP is a code for building E(3)-equivariant interatomic potentials
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
About JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ https://www.youtube.com/@dr_k_choudhary
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
Generate input parameters and coordinates for atomistic and coarse-grained simulations of polymers, ssDNA, and carbohydrates
Python tool to manipulate Gaussian cube files
Train, fine-tune, and manipulate machine learning models for atomistic systems
A Python Package to Automate Thermodynamic Integration Calculations for Free Energy
A collection of simulation recipes for the atomic-scale modeling of materials and molecules
Grand canonical optimization of grain boundary phases.
A collection of Python scripts for computing physical properties and analyzing trajectories from molecular dynamics simulations.
Toolkit using the Atomistic Simulation Environment (ASE)
Utilities for ab initio modeling suite CRYSTAL, developed in Turin University
Interface enabling use of ANI-style, and other NN-IPs in the Amber molecular dynamics software suite. Works with both Amber engines, sander and pmemd.
JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ
Input file writers and output file readers for the density functional theory code CASTEP.
Protein chemical shift prediction with PyTorch
Library for handling atomistic graph datasets focusing on transformer-based implementations, with utilities for training various models, experimenting with different pre-training tasks, and a suite of pre-trained models with huggingface integrations
A python package for fast building amorphous solids and liquid mixtures from @materialsproject computed structures and machine learning interatomic potentials
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