bytedance / bambooLinks
BAMBOO (Bytedance AI Molecular BOOster) is an AI-driven machine learning force field designed for precise and efficient electrolyte simulations.
☆148Updated 3 months ago
Alternatives and similar repositories for bamboo
Users that are interested in bamboo are comparing it to the libraries listed below
Sorting:
- AI-enhanced computational chemistry☆131Updated last month
- Official implementation of DeepDFT model☆87Updated 2 years ago
- A Python software package for saddle point optimization and minimization of atomic systems.☆132Updated 3 weeks ago
- A collection of Neural Network Models for chemistry☆183Updated last week
- ☆118Updated this week
- LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials☆59Updated 4 months ago
- SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.☆217Updated last week
- AI for crystal materials☆112Updated last week
- Ionic liquid force field parameters (OPLS-2009IL and OPLS-VSIL)☆73Updated last year
- MACE foundation models (MP, OMAT, mh-1)☆200Updated 2 months ago
- SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS☆139Updated 3 weeks ago
- A Large Language Model of the CIF format for Crystal Structure Generation☆150Updated 4 months ago
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆31Updated 5 years ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆82Updated last month
- Code for automated fitting of machine learned interatomic potentials.☆137Updated last week
- Matbench: Benchmarks for materials science property prediction☆188Updated last year
- Universal Transfer Learning in Porous Materials, including MOFs.☆115Updated last year
- Gromacs to Lammps simulation converter☆90Updated 2 years ago
- A unified framework for machine learning collective variables for enhanced sampling simulations☆134Updated last week
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆85Updated 3 years ago
- A repository for implementing graph network models based on atomic structures.☆104Updated last year
- DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules☆30Updated last year
- [ICLR 2024] The implementation for the paper "Space Group Constrained Crystal Generation"☆61Updated last week
- This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials☆25Updated 3 years ago
- ☆32Updated 2 years ago
- train and use graph-based ML models of potential energy surfaces☆121Updated last month
- DeePMD-kit plugin for various graph neural network models☆52Updated this week
- FTCP code☆36Updated 2 years ago
- ☆69Updated 4 years ago
- ☆52Updated 3 years ago