learningmatter-mit / NeuralForceField
Neural Network Force Field based on PyTorch
☆269Updated this week
Alternatives and similar repositories for NeuralForceField:
Users that are interested in NeuralForceField are comparing it to the libraries listed below
- GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆199Updated last year
- SchNet - a deep learning architecture for quantum chemistry☆246Updated 6 years ago
- Training neural network potentials☆395Updated last week
- OpenMM plugin to define forces with neural networks☆194Updated 2 months ago
- [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations☆262Updated 2 months ago
- End-To-End Molecular Dynamics (MD) Engine using PyTorch☆617Updated 3 months ago
- Workflow for creating and analyzing the Open Catalyst Dataset☆106Updated 2 months ago
- Pytorch differentiable molecular dynamics☆175Updated 2 years ago
- NequIP is a code for building E(3)-equivariant interatomic potentials☆710Updated last month
- Torch-native, batchable, atomistic simulation☆198Updated this week
- sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model☆147Updated last year
- G-SchNet - a generative model for 3d molecular structures☆135Updated 2 years ago
- [TMLR 2023] Training and simulating MD with ML force fields☆112Updated 5 months ago
- Build neural networks for machine learning force fields with JAX☆113Updated 2 months ago
- Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials☆387Updated 3 weeks ago
- Converts an xyz file to an RDKit mol object☆264Updated 3 months ago
- Graph deep learning library for materials☆335Updated last week
- A repository of update in molecular dynamics field by recent progress in machine learning and deep learning.☆316Updated 4 years ago
- DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable implementation of molecular…☆173Updated 2 weeks ago
- Matbench: Benchmarks for materials science property prediction☆152Updated 8 months ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆169Updated this week
- cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffD…☆210Updated this week
- MatDeepLearn, package for graph neural networks in materials chemistry☆188Updated 2 years ago
- Official code for Periodic Graph Transformers for Crystal Material Property Prediction (NeurIPS 2022)☆93Updated last year
- DimeNet and DimeNet++ models, as proposed in "Directional Message Passing for Molecular Graphs" (ICLR 2020) and "Fast and Uncertainty-Awa…☆317Updated last year
- An object-aware diffusion model for generating chemical reactions☆124Updated 10 months ago
- An SE(3)-invariant autoencoder for generating the periodic structure of materials [ICLR 2022]☆289Updated 8 months ago
- MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.☆686Updated this week
- Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary☆266Updated last week
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆72Updated 2 years ago