Luthaf / vesinLinks
Compute neighbor lists for atomistic systems
☆53Updated 2 weeks ago
Alternatives and similar repositories for vesin
Users that are interested in vesin are comparing it to the libraries listed below
Sorting:
- Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics☆56Updated this week
- Particle-mesh based calculations of long-range interactions in PyTorch☆49Updated this week
- Quick Uncertainty and Entropy via STructural Similarity☆42Updated 3 weeks ago
- python workflow toolkit☆39Updated 3 months ago
- Alchemical machine learning interatomic potentials☆29Updated 6 months ago
- ☆16Updated last week
- ☆21Updated last year
- ☆23Updated last year
- ☆25Updated last year
- MACE_Osaka24 models☆14Updated 5 months ago
- ☆18Updated 2 years ago
- Training and evaluating machine learning models for atomistic systems.☆32Updated this week
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆16Updated 7 months ago
- Tools for machine learnt interatomic potentials☆29Updated this week
- Collection of Tutorials on Machine Learning Interatomic Potentials☆18Updated 10 months ago
- Heat-conductivity benchmark test for foundational machine-learning potentials☆23Updated 3 months ago
- ⚛ download and manipulate atomistic datasets☆44Updated 5 months ago
- Some tutorial-style examples for validating machine-learned interatomic potentials☆34Updated last year
- Particle-mesh based calculations of long-range interactions in JAX☆17Updated 3 months ago
- GRACE models and gracemaker (as implemented in TensorPotential package)☆57Updated 2 months ago
- Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response)☆37Updated last week
- Cross-platform Optimizer for ML Interatomic Potentials☆18Updated 6 months ago
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆81Updated last week
- Collection of tutorials to use the MACE machine learning force field.☆46Updated 8 months ago
- Computing representations for atomistic machine learning☆71Updated last week
- Display and Edit Molecules (https://zndraw.icp.uni-stuttgart.de)☆42Updated last week
- ☆29Updated 4 months ago
- ☆21Updated this week
- A foundational potential energy dataset for materials☆35Updated this week
- A molecular simulation package integrating MLFFs in MOFs for DAC☆27Updated last month