ilyes319 / mace-tutorialsLinks
Collection of tutorials to use the MACE machine learning force field.
☆50Updated last year
Alternatives and similar repositories for mace-tutorials
Users that are interested in mace-tutorials are comparing it to the libraries listed below
Sorting:
- ☆32Updated 2 months ago
- ☆33Updated last week
- Quick Uncertainty and Entropy via STructural Similarity☆54Updated last week
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆84Updated this week
- Active Learning for Machine Learning Potentials☆63Updated last month
- Collection of Tutorials on Machine Learning Interatomic Potentials☆23Updated last year
- ☆110Updated last week
- train and use graph-based ML models of potential energy surfaces☆117Updated last week
- PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integr…☆54Updated last month
- GRACE models and gracemaker (as implemented in TensorPotential package)☆77Updated last week
- Particle-mesh based calculations of long-range interactions in PyTorch☆65Updated last week
- MACE_Osaka24 models☆23Updated last year
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆96Updated 2 weeks ago
- Alchemical machine learning interatomic potentials☆32Updated last year
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆68Updated 3 weeks ago
- python workflow toolkit☆45Updated this week
- Deprecated - see `pair_nequip_allegro`☆44Updated 8 months ago
- Code for automated fitting of machine learned interatomic potentials.☆133Updated last week
- ⚛ download and manipulate atomistic datasets☆48Updated last month
- Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response)☆40Updated last week
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Updated last year
- A framework for performing active learning for training machine-learned interatomic potentials.☆39Updated last month
- ☆21Updated last year
- OVITO Python modifier to compute the Warren-Cowley parameters.☆37Updated 8 months ago
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆50Updated last week
- ☆28Updated 5 months ago
- ☆24Updated last year
- Compute neighbor lists for atomistic systems☆68Updated 2 weeks ago
- A Python software package for saddle point optimization and minimization of atomic systems.☆123Updated 3 months ago
- A molecular simulation package integrating MLFFs in MOFs for DAC☆41Updated 2 months ago