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:
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆21Updated 11 months ago
- This repo contains the datasets used for the paper The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials.☆19Updated 3 years ago
- Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting☆34Updated last year
- Implementing PaiNN in Pytorch Geometric☆15Updated 3 years ago
- [NeurIPS 2024] source code for "A Recipe for Charge Density Prediction"☆36Updated 9 months ago
- ☆12Updated 5 years ago
- GemNet model in TensorFlow, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆27Updated 2 years ago
- A Newtonian message passing network for deep learning of interatomic potentials and forces☆45Updated 3 months ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆78Updated 3 years ago
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆27Updated 5 years ago
- Robust NN MD simulator☆20Updated 2 years ago
- ☆33Updated 3 weeks ago
- Predict the electronic structure and atomic properties (potential energy, forces, and stress tensor) of polymers containing N and/or O.☆22Updated last year
- Code for performing adversarial attacks on atomistic systems using NN potentials☆40Updated 3 years ago
- Equivariant network to predict activation barriers and molecular orbitals through coefficients of molecular orbitals.☆11Updated last year
- ☆20Updated 3 years ago
- Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022☆53Updated 3 years ago
- A framework for performing active learning for training machine-learned interatomic potentials.☆39Updated 2 weeks ago
- ☆11Updated 2 years ago
- Generate and predict molecular electron densities with Euclidean Neural Networks☆48Updated 2 years ago
- Collection of tutorials to use the MACE machine learning force field.☆48Updated last year
- The architector python package - for 3D metal complex design. C22085☆68Updated last month
- Basis set optimization library for quantum chemistry☆35Updated 3 months ago
- ☆12Updated 6 months ago
- An overview of literature that discusses the use of machine learning for atomistic simulations☆45Updated 2 years ago
- Official implementation of DeepDFT model☆84Updated 2 years ago
- ☆61Updated last month
- [ICLR 2025] Official Implementation of "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy …☆20Updated 5 months ago
- Deprecated - see `pair_nequip_allegro`☆44Updated 5 months ago
- Alchemical machine learning interatomic potentials☆32Updated 11 months ago