¿Cuáles son las características principales de learningmatter-mit/neuralforcefield?

Las características principales de learningmatter-mit/neuralforcefield son: Interatomic Potentials.

¿Qué alternativas de código abierto existen para learningmatter-mit/neuralforcefield?

Las alternativas de código abierto para learningmatter-mit/neuralforcefield incluyen: lammps/lammps — This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements… acesuit/acefit.jl — Generic Codes for Fitting ACE models. acesuit/mace — MACE - Table of contents - About MACE - Documentation - Installation - pip installation - pip installation from source… aiqm/torchani — TorchANI 2.0 is an open-source library that supports training, development, and research of ANI-style neural network… apax-hub/apax — apax[^1][^2] is a high-performance, extendable package for training of and inference with atomistic neural networks.… acesuit/ace1.jl — Notes: This is currently a development branch of ACE (though we are still tagging versions regularly). For the latest…

NeuralForceField - train neural network force

Name: learningmatter-mit/neuralforcefield
Author: learningmatter-mit

The Neural Force Field (NFF) code is an API based on SchNet [1-4], DimeNet [5], PaiNN [6-7] and DANN [8]. It provides an interface to train and evaluate neural networks for force fields. It can also be used as a property predictor that uses both 3D geometries and 2D graph information [9].

Features

Interatomic Potentials - PyTorch-based neural network force field implementation.

Historial de estrellas

Features

Interatomic Potentials - PyTorch-based neural network force field implementation.

lammps/lammps

2,783Ver en GitHub

This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements of atoms and molecules. It functions as an interatomic potential framework for calculating forces between particles and a materials analysis tool for computing thermodynamic, structural, and transport properties of solids and fluids. The engine is distinguished by its high-performance computing capabilities, utilizing spatial-domain decomposition and message-passing interface communication to distribute workloads across processors. It supports multi-backend GPU acceleration v

ACEsuit/ACEfit.jl

8Ver en GitHub

Generic Codes for Fitting ACE models

ACEsuit/mace

1,253Ver en GitHub

MACE - Table of contents - About MACE - Documentation - Installation - pip installation - pip installation from source - Usage - Training - Evaluation - Tutorials - CUDA acceleration with cuEquivariance - Weights and Biases for experiment tracking - Pretrained Foundation Models - MACE-MP:…

ACEsuit/ACE1.jl

23Ver en GitHub

Notes: This is currently a development branch of ACE (though we are still tagging versions regularly). For the latest stable version see DEV-v0.8.x Preliminary Documentation, WIP.