ACEsuit / mace-off
MACE-OFF23 models
☆31Updated last month
Alternatives and similar repositories for mace-off:
Users that are interested in mace-off are comparing it to the libraries listed below
- Particle-mesh based calculations of long-range interactions in PyTorch☆33Updated last week
- MLP training for molecular systems☆42Updated this week
- Algorithms to analyze and predict molecular structures☆16Updated 6 months ago
- tmQM dataset files☆50Updated 6 months ago
- ☆24Updated 10 months ago
- ☆48Updated 2 months ago
- Efficient And Fully Differentiable Extended Tight-Binding☆84Updated this week
- Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…☆70Updated last week
- train and use graph-based ML models of potential energy surfaces☆71Updated last week
- ⚛ download and manipulate atomistic datasets☆40Updated 2 months ago
- Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics☆56Updated 2 weeks ago
- An automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials☆15Updated 4 months ago
- Generate and predict molecular electron densities with Euclidean Neural Networks☆45Updated last year
- SO3krates and Universal Pairwise Force Field for Molecular Simulation☆80Updated 3 weeks ago
- Collection of tutorials to use the MACE machine learning force field.☆43Updated 5 months ago
- A text-guided diffusion model for crystal structure generation☆37Updated 2 weeks ago
- AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials☆15Updated 4 years ago
- Force-field-enhanced Neural Networks optimized library☆28Updated this week
- ☆41Updated 2 years ago
- This is a simple but efficient implementation of PaiNN-model for constructing machine learning interatomic potentials☆16Updated 2 years ago
- LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support☆41Updated 4 months ago
- Supporting material for the paper "Data driven collective variables for enhanced sampling"☆18Updated 8 months ago
- Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons☆15Updated 4 months ago
- AI-enhanced computational chemistry☆77Updated 2 months ago
- ☆62Updated last week
- Compute neighbor lists for atomistic systems☆39Updated 2 weeks ago
- Collection of Tutorials on Machine Learning Interatomic Potentials☆18Updated 7 months ago
- Material for the 3rd i-CoMSE Workshop: Methods for Advanced Sampling☆37Updated last year
- “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…☆26Updated 4 years ago
- MACE_Osaka24 models☆14Updated 2 months ago