aiqm/torchani
TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network interatomic potentials. It was originally developed and is currently maintained by the Roitberg group.
This tool helps computational chemists and materials scientists predict atomic forces and energies using neural network models. You input molecular structures or atomic configurations, and it provides highly accurate energy and force calculations for use in simulations. It is designed for researchers who work with molecular dynamics and quantum chemistry problems.
540 stars. Used by 1 other package. Available on PyPI.
Use this if you need to rapidly and accurately calculate interatomic potentials for molecular simulations or material property predictions.
Not ideal if you are looking for a general-purpose machine learning library or if you primarily work with classical force fields without neural network integration.
Stars
540
Forks
137
Language
Python
License
MIT
Category
Last pushed
Mar 04, 2026
Commits (30d)
0
Dependencies
13
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aiqm/torchani"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
deepmodeling/deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
chemprop/chemprop
Message Passing Neural Networks for Molecule Property Prediction
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
Acellera/moleculekit
MoleculeKit: Your favorite molecule manipulation kit
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling...