ACEsuit / mace-tutorialsLinks
Collection of tutorials to use the MACE machine learning force field.
☆51Updated this week
Alternatives and similar repositories for mace-tutorials
Users that are interested in mace-tutorials are comparing it to the libraries listed below
Sorting:
- ☆40Updated last month
- ☆32Updated 3 months ago
- Active Learning for Machine Learning Potentials☆63Updated 2 months ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆70Updated last week
- Quick Uncertainty and Entropy via STructural Similarity☆55Updated 2 weeks ago
- ☆117Updated 2 weeks ago
- Alchemical machine learning interatomic potentials☆32Updated last year
- 🌟 [NeurIPS '25 Spotlight] Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics …☆87Updated last month
- Collection of Tutorials on Machine Learning Interatomic Potentials☆24Updated last year
- Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response)☆41Updated 2 weeks ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆81Updated last month
- Computing representations for atomistic machine learning☆75Updated this week
- train and use graph-based ML models of potential energy surfaces☆119Updated last month
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆96Updated last month
- MACE_Osaka24 models☆25Updated last year
- Train, fine-tune, and manipulate machine learning models for atomistic systems☆52Updated this week
- A framework for performing active learning for training machine-learned interatomic potentials.☆39Updated 2 months ago
- python workflow toolkit☆43Updated last month
- A unified platform for fine-tuning atomistic foundation models in chemistry and materials science☆71Updated last month
- Code for automated fitting of machine learned interatomic potentials.☆133Updated last week
- PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integr…☆55Updated 2 months ago
- LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials☆59Updated 4 months ago
- Compute neighbor lists for atomistic systems☆71Updated 2 weeks ago
- Deprecated - see `pair_nequip_allegro`☆44Updated 9 months ago
- A flexible workflow for on-the-fly learning of interatomic potential models.☆31Updated this week
- ⚛ download and manipulate atomistic datasets☆48Updated 2 months ago
- A molecular simulation package integrating MLFFs in MOFs for DAC☆41Updated 3 months ago
- Training Neural Network potentials through customizable routines in JAX.☆59Updated 5 months ago
- Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022☆53Updated 3 years ago
- ☆21Updated last year