basf / mlipx
Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIPs). It offers a growing set of evaluation methods alongside powerful visualization and comparison tools.
☆73Updated last month
Alternatives and similar repositories for mlipx:
Users that are interested in mlipx are comparing it to the libraries listed below
- A repository to hold installation recipes and data files for the PET-MAD universal machine-learning interatomic potential☆36Updated this week
- Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics☆56Updated this week
- ⚛ download and manipulate atomistic datasets☆43Updated 3 months ago
- Particle-mesh based calculations of long-range interactions in PyTorch☆35Updated 2 weeks ago
- Efficient And Fully Differentiable Extended Tight-Binding☆84Updated last week
- MLP training for molecular systems☆46Updated this week
- ☆24Updated 11 months ago
- train and use graph-based ML models of potential energy surfaces☆78Updated this week
- GRACE models and gracemaker (as implemented in TensorPotential package)☆51Updated last week
- Compute neighbor lists for atomistic systems☆49Updated 2 weeks ago
- MACE-OFF23 models☆31Updated last month
- Quick Uncertainty and Entropy via STructural Similarity☆34Updated last week
- python workflow toolkit☆37Updated last month
- ☆61Updated last week
- Collection of tutorials to use the MACE machine learning force field.☆43Updated 6 months ago
- Python package to interact with high-dimensional representations of the chemical elements☆40Updated last week
- Code for automated fitting of machine learned interatomic potentials.☆71Updated last week
- Computing representations for atomistic machine learning☆67Updated last month
- Display and Edit Molecules (https://zndraw.icp.uni-stuttgart.de)☆38Updated last week
- An ecosystem for digital reticular chemistry☆47Updated 6 months ago
- Active Learning for Machine Learning Potentials☆52Updated 10 months ago
- Collection of Tutorials on Machine Learning Interatomic Potentials☆18Updated 7 months ago
- Algorithms to analyze and predict molecular structures☆16Updated 6 months ago
- A framework for performing active learning for training machine-learned interatomic potentials.☆32Updated 4 months ago
- open data sets for machine learning pertaining to porous materials☆27Updated last year
- Force-field-enhanced Neural Networks optimized library☆28Updated 3 weeks ago
- A fully featured ASE calculator for xTB☆17Updated 5 months ago
- A molecular simulation package integrating MLFFs in MOFs for DAC☆24Updated 2 weeks ago
- Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response)☆31Updated last week
- LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support☆41Updated 5 months ago