mir-group / nequip-input-filesLinks
Input files for Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., ... & Kozinsky, B. (2021). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv preprint arXiv:2101.03164.
☆13Updated 3 months ago
Alternatives and similar repositories for nequip-input-files
Users that are interested in nequip-input-files are comparing it to the libraries listed below
Sorting:
- This repo contains the datasets used for the paper The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.☆19Updated 3 years ago
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆21Updated last year
- A Newtonian message passing network for deep learning of interatomic potentials and forces☆45Updated 4 months ago
- Implementing PaiNN in Pytorch Geometric☆15Updated 3 years ago
- Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting☆35Updated last year
- [NeurIPS 2024] source code for "A Recipe for Charge Density Prediction"☆36Updated 10 months ago
- Robust NN MD simulator☆20Updated 2 years ago
- GemNet model in TensorFlow, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆27Updated 2 years ago
- ☆12Updated 5 years ago
- ☆21Updated 3 years ago
- ☆12Updated 2 years ago
- Equivariant network to predict activation barriers and molecular orbitals through coefficients of molecular orbitals.☆12Updated last year
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆28Updated 5 years ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆79Updated 3 years ago
- Code for performing adversarial attacks on atomistic systems using NN potentials☆40Updated 3 years ago
- Predict the electronic structure and atomic properties (potential energy, forces, and stress tensor) of polymers containing N and/or O.☆22Updated last year
- ☆34Updated last month
- ☆12Updated 7 months ago
- Generate and predict molecular electron densities with Euclidean Neural Networks☆48Updated 2 years ago
- AutoTST: A framework to perform automated transition state theory calculations☆44Updated last year
- An overview of literature that discusses the use of machine learning for atomistic simulations☆45Updated 2 years ago
- NN PES for reactions.☆11Updated 3 years ago
- Flow matching for accelerated simulation of atomic transport☆37Updated last week
- ☆23Updated 3 years ago
- Training Neural Network potentials through customizable routines in JAX.☆54Updated 3 months ago
- [ICLR 2025] Official Implementation of "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy …☆20Updated 6 months ago
- Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022☆53Updated 3 years ago
- ☆25Updated 3 years ago
- Alchemical machine learning interatomic potentials☆32Updated 11 months ago
- tools for machine learning in condensed matter physics and quantum chemistry☆33Updated 3 years ago