Alternatives and similar repositories for aseMolec

Users that are interested in aseMolec are comparing it to the libraries listed below

• ilyes319 / mace-tutorials
  Collection of tutorials to use the MACE machine learning force field.
  ☆48Updated last year
• lab-cosmo / torch-pme
  Particle-mesh based calculations of long-range interactions in PyTorch
  ☆62Updated last week
• BingqingCheng / cace
  ☆101Updated last week
• jla-gardner / graph-pes
  train and use graph-based ML models of potential energy surfaces
  ☆110Updated 2 weeks ago
• basf / mlipx
  Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…
  ☆96Updated last week
• mir-group / pair_nequip_allegro
  LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials
  ☆53Updated last month
• ICAMS / grace-tensorpotential
  GRACE models and gracemaker (as implemented in TensorPotential package)
  ☆74Updated last month
• zadorlab / sella
  A Python software package for saddle point optimization and minimization of atomic systems.
  ☆120Updated last month
• ulissigroup / finetuna
  Active Learning for Machine Learning Potentials
  ☆59Updated 2 months ago
• autoatml / autoplex
  Code for automated fitting of machine learned interatomic potentials.
  ☆126Updated 2 weeks ago
• JoshRackers / equivariant_electron_density
  Generate and predict molecular electron densities with Euclidean Neural Networks
  ☆48Updated 2 years ago
• peterbjorgensen / DeepDFT
  Official implementation of DeepDFT model
  ☆84Updated 2 years ago
• hspark1212 / chemeleon
  A text-guided diffusion model for crystal structure generation
  ☆64Updated 4 months ago
• Shen-Group / E2GNN
  ☆20Updated 11 months ago
• ACEsuit / mace-off
  MACE-OFF23 models
  ☆48Updated 8 months ago
• Fung-Lab / MatterTune
  A unified platform for fine-tuning atomistic foundation models in chemistry and materials science
  ☆63Updated 2 weeks ago
• atomind-ai / mlip-arena
  Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://arxiv.org/abs/2509.…
  ☆77Updated this week
• ASK-Berkeley / MLFF-distill
  [ICLR 2025] Official Implementation of "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy …
  ☆20Updated 6 months ago
• ChengUCB / les
  ☆27Updated last month
• dskoda / quests
  Quick Uncertainty and Entropy via STructural Similarity
  ☆50Updated 3 weeks ago
• imagdau / Tutorials
  Collection of Tutorials on Machine Learning Interatomic Potentials
  ☆22Updated last year
• Yangxinsix / curator
  A flexible workflow for on-the-fly learning of interatomic potential models.
  ☆31Updated this week
• andreagrisafi / SALTED
  Symmetry-Adapted Learning of Three-dimensional Electron Densities (and their electrostatic response)
  ☆40Updated 2 weeks ago
• Ramprasad-Group / ML-DFT
  Predict the electronic structure and atomic properties (potential energy, forces, and stress tensor) of polymers containing N and/or O.
  ☆22Updated last year
• materialsvirtuallab / matpes
  A foundational potential energy dataset for materials
  ☆43Updated 2 weeks ago
• Arif-PhyChem / datasets_and_databases_4_MLPs
  ☆61Updated 2 months ago
• BingqingCheng / ab-initio-thermodynamics-of-water
  “Ab initio thermodynamics of liquid and solid water” Bingqing Cheng, Edgar A. Engel, JÖrg Behler, Christoph Dellago and Michele Ceriotti…
  ☆28Updated 5 years ago
• lanl / ALF
  A framework for performing active learning for training machine-learned interatomic potentials.
  ☆39Updated last month
• grimme-lab / dxtb
  Efficient And Fully Differentiable Extended Tight-Binding
  ☆104Updated this week
• killiansheriff / WarrenCowleyParameters
  OVITO Python modifier to compute the Warren-Cowley parameters.
  ☆34Updated 6 months ago