m-k-S / awesome-machine-learning-atomistic-simulationLinks
An overview of literature that discusses the use of machine learning for atomistic simulations
☆45Updated 2 years ago
Alternatives and similar repositories for awesome-machine-learning-atomistic-simulation
Users that are interested in awesome-machine-learning-atomistic-simulation are comparing it to the libraries listed below
Sorting:
- The architector python package - for 3D metal complex design. C22085☆70Updated last month
- Moment Invariants Local Atomic Descriptor☆33Updated last year
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆79Updated 3 years ago
- Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022☆53Updated 3 years ago
- Machine learning exercises for the MolSim course (http://www.acmm.nl/molsim/molsim2023/index.html)☆28Updated last year
- Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials".☆21Updated 11 months ago
- Official implementation of DeepDFT model☆84Updated 2 years ago
- GemNet model in TensorFlow, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)☆27Updated 2 years ago
- Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Palette.☆37Updated 2 years ago
- open data sets for machine learning pertaining to porous materials☆27Updated last year
- Collection of tutorials to use the MACE machine learning force field.☆48Updated last year
- Equivariant network to predict activation barriers and molecular orbitals through coefficients of molecular orbitals.☆11Updated last year
- A framework for performing active learning for training machine-learned interatomic potentials.☆39Updated last month
- MLP training for molecular systems☆54Updated last month
- Generate and predict molecular electron densities with Euclidean Neural Networks☆48Updated 2 years ago
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆96Updated last week
- Deprecated - see `pair_nequip_allegro`☆44Updated 6 months ago
- ☆34Updated last month
- A software for automating materials science computations☆33Updated this week
- Predict the electronic structure and atomic properties (potential energy, forces, and stress tensor) of polymers containing N and/or O.☆22Updated last year
- Training Neural Network potentials through customizable routines in JAX.☆52Updated 2 months ago
- Active Learning for Machine Learning Potentials☆59Updated 2 months ago
- an interface to semi-empirical quantum chemistry methods implemented with pytorch☆62Updated this week
- Automated workflow for generating quantum chemistry calculation of explicitly solvated molecules☆54Updated last month
- An ecosystem for digital reticular chemistry☆51Updated last year
- ⚛ download and manipulate atomistic datasets☆47Updated last week
- Equivariant machine learning interatomic potentials in JAX.☆76Updated this week
- Strategies for the Construction of Neural-Network Based Machine-Learning Potentials (MLPs)☆28Updated 4 years ago
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆40Updated last year
- ☆61Updated last week