Alternatives and similar repositories for load-atoms

Users that are interested in load-atoms are comparing it to the libraries listed below

• atomind-ai / mlip-arena
  Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics https://openreview.net/foru…
  ☆60Updated 3 weeks ago
• basf / mlipx
  Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…
  ☆89Updated last month
• lab-cosmo / torch-pme
  Particle-mesh based calculations of long-range interactions in PyTorch
  ☆53Updated last week
• Fung-Lab / MatterTune
  A unified platform for fine-tuning atomistic foundation models in chemistry and materials science
  ☆54Updated last week
• libAtoms / workflow
  python workflow toolkit
  ☆40Updated 2 weeks ago
• jla-gardner / graph-pes
  train and use graph-based ML models of potential energy surfaces
  ☆99Updated last week
• qiqb-osaka / mace-osaka24
  MACE_Osaka24 models
  ☆16Updated 7 months ago
• imagdau / aseMolec
  ☆28Updated 2 weeks ago
• dskoda / quests
  Quick Uncertainty and Entropy via STructural Similarity
  ☆47Updated last month
• ChengUCB / les
  ☆23Updated last month
• Arif-PhyChem / datasets_and_databases_4_MLPs
  ☆59Updated 2 months ago
• duartegroup / mlp-train
  MLP training for molecular systems
  ☆49Updated this week
• Luthaf / vesin
  Compute neighbor lists for atomistic systems
  ☆57Updated this week
• grimme-lab / dxtb
  Efficient And Fully Differentiable Extended Tight-Binding
  ☆98Updated 3 weeks ago
• MPA2suite / k_SRME
  Heat-conductivity benchmark test for foundational machine-learning potentials
  ☆23Updated 2 weeks ago
• ilyes319 / mace-tutorials
  Collection of tutorials to use the MACE machine learning force field.
  ☆47Updated 10 months ago
• ICAMS / grace-tensorpotential
  GRACE models and gracemaker (as implemented in TensorPotential package)
  ☆65Updated last month
• CSIprinceton / workshop-july-2022
  Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022
  ☆52Updated 3 years ago
• basf / autoadsorbate
  Chemical intuition for surface science in a package.
  ☆36Updated last week
• grimme-lab / g-xtb
  Development versions of the g-xTB method. Final implementation will not happen here but in tblite (https://github.com/tblite/tblite).
  ☆88Updated last month
• autoatml / autoplex
  Code for automated fitting of machine learned interatomic potentials.
  ☆86Updated this week
• metatensor / featomic
  Computing representations for atomistic machine learning
  ☆72Updated this week
• Andrew-S-Rosen / xtb_ase
  A fully featured ASE calculator for xTB
  ☆21Updated 9 months ago
• ulissigroup / finetuna
  Active Learning for Machine Learning Potentials
  ☆56Updated last year
• WMD-group / ElementEmbeddings
  Python package to interact with high-dimensional representations of the chemical elements
  ☆43Updated this week
• metatensor / metatrain
  Training and evaluating machine learning models for atomistic systems.
  ☆36Updated this week
• stfc / janus-core
  Tools for machine learnt interatomic potentials
  ☆33Updated last week
• dft-dutoit / XPOT
  Cross-platform Optimizer for ML Interatomic Potentials
  ☆19Updated 3 weeks ago
• MorrowChem / how-to-validate-potentials
  Some tutorial-style examples for validating machine-learned interatomic potentials
  ☆34Updated last year
• MPA2suite / autoWTE
  Benchmarking foundation Machine Learning Potentials with Lattice Thermal Conductivity from Anharmonic Phonons
  ☆16Updated 9 months ago