AIforGreatGood / charge3netLinks
[npj Comp. Mat.] Higher-order equivariant neural networks for charge density prediction in materials
☆65Updated 8 months ago
Alternatives and similar repositories for charge3net
Users that are interested in charge3net are comparing it to the libraries listed below
Sorting:
- [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential☆56Updated last month
- train and use graph-based ML models of potential energy surfaces☆112Updated this week
- Official implementation of DeepDFT model☆85Updated 2 years ago
- Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"☆79Updated 3 years ago
- LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials☆56Updated last month
- Collection of tutorials to use the MACE machine learning force field.☆49Updated last year
- A text-guided diffusion model for crystal structure generation☆66Updated 5 months ago
- ☆30Updated last month
- Generative materials benchmarking metrics, inspired by guacamol and CDVAE.☆40Updated last year
- [NeurIPS 2024] source code for "A Recipe for Charge Density Prediction"☆36Updated 10 months ago
- ☆106Updated 2 weeks ago
- A repository for implementing graph network models based on atomic structures.☆95Updated last year
- Generate and predict molecular electron densities with Euclidean Neural Networks☆48Updated 2 years ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆75Updated 2 weeks ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆63Updated 3 weeks ago
- Build neural networks for machine learning force fields with JAX☆125Updated 5 months ago
- Code for automated fitting of machine learned interatomic potentials.☆127Updated last week
- FTCP code☆35Updated 2 years ago
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆65Updated 2 weeks ago
- ☆62Updated last week
- A foundational potential energy dataset for materials☆45Updated last week
- Active Learning for Machine Learning Potentials☆59Updated 3 months ago
- ☆29Updated 3 years ago
- Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://openreview.net/foru…☆78Updated 3 weeks ago
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆29Updated 5 years ago
- MACE-OFF23 models☆53Updated 9 months ago
- [ICLR 2025] Official Implementation of "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy …☆21Updated 6 months ago
- Reference implementation of "Ewald-based Long-Range Message Passing for Molecular Graphs" (ICML 2023)☆51Updated 2 years ago
- This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials☆23Updated 2 years ago
- ☆29Updated last week