Open-Catalyst-Project / AdsorbMLLinks
Corresponding dataset and tools for the AdsorbML manuscript.
☆41Updated 8 months ago
Alternatives and similar repositories for AdsorbML
Users that are interested in AdsorbML are comparing it to the libraries listed below
Sorting:
- Workflow for creating and analyzing the Open Catalyst Dataset☆113Updated 8 months ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆78Updated 3 years ago
- AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch☆60Updated 2 years ago
- Deprecated - see `pair_nequip_allegro`☆44Updated 5 months ago
- Unsupervised learning of atomic scale dynamics from molecular dynamics.☆81Updated 3 years ago
- Higher-order equivariant neural networks for charge density prediction in materials☆62Updated 7 months ago
- Official implementation of DeepDFT model☆84Updated 2 years ago
- Integer Programming encoding for Crystal Structure Prediction with classic and quantum computing bindings☆49Updated 2 years ago
- Collection of tutorials to use the MACE machine learning force field.☆48Updated last year
- A framework for performing active learning for training machine-learned interatomic potentials.☆39Updated 2 weeks ago
- Space Group Informed Transformer for Crystalline Materials Generation☆118Updated 2 months ago
- A Python library for building atomic neural networks☆117Updated 5 months ago
- Code Repository for "Direct prediction of phonon density of states with Euclidean neural network"☆28Updated 3 years ago
- Matbench: Benchmarks for materials science property prediction☆167Updated last year
- A repository for implementing graph network models based on atomic structures.☆94Updated last year
- a package for developing machine learning-based chemically accurate energy and density functional models☆113Updated 5 months ago
- ☆29Updated 3 years ago
- ☆22Updated 3 years ago
- LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials☆53Updated 3 weeks ago
- Generating Deep Potential with Python☆69Updated 3 weeks ago
- ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.☆149Updated last year
- Active Learning for Machine Learning Potentials☆58Updated last month
- BAMBOO (Bytedance AI Molecular BOOster) is an AI-driven machine learning force field designed for precise and efficient electrolyte simu…☆125Updated 5 months ago
- A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w☆71Updated last month
- Code for automated fitting of machine learned interatomic potentials.☆126Updated this week
- ☆30Updated last week
- [TMLR 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields☆38Updated 8 months ago
- Source code for generating materials with 20 space groups using PGCGM☆33Updated 2 years ago
- KIM-based Learning-Integrated Fitting Framework for interatomic potentials.☆38Updated last week
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆27Updated 5 years ago