SAITPublic / MLFF-FrameworkLinks
This repository is a package to provide SAIT Machine Learning Force Field(MLFF) Framework
☆38Updated last year
Alternatives and similar repositories for MLFF-Framework
Users that are interested in MLFF-Framework are comparing it to the libraries listed below
Sorting:
- ☆83Updated this week
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆187Updated last week
- train and use graph-based ML models of potential energy surfaces☆99Updated 2 weeks ago
- Higher-order equivariant neural networks for charge density prediction in materials☆59Updated 4 months ago
- ☆43Updated last year
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆77Updated 3 years ago
- A text-guided diffusion model for crystal structure generation☆62Updated last month
- Code for automated fitting of machine learned interatomic potentials.☆85Updated this week
- LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials☆47Updated this week
- Official implementation of DeepDFT model☆80Updated 2 years ago
- Collection of tutorials to use the MACE machine learning force field.☆47Updated 10 months ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆61Updated 3 weeks ago
- A Large Language Model of the CIF format for Crystal Structure Generation☆112Updated 6 months ago
- MACE foundation models (MP, OMAT, Matpes)☆133Updated 2 weeks ago
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆40Updated last year
- [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential☆51Updated 4 months ago
- SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS☆114Updated 3 months ago
- Collection of Tutorials on Machine Learning Interatomic Potentials☆19Updated 11 months ago
- ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-learning Rationalization of Hydrothermal Parameters (ACS Central Scien…☆26Updated 8 months ago
- Active Learning for Machine Learning Potentials☆55Updated last year
- An evaluation framework for machine learning models simulating high-throughput materials discovery.☆170Updated this week
- BAMBOO (Bytedance AI Molecular BOOster) is an AI-driven machine learning force field designed for precise and efficient electrolyte simu…☆111Updated 2 months ago
- A foundational potential energy dataset for materials