Alternatives and similar repositories for mess

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

• ACEsuit / mace-jax
  Equivariant machine learning interatomic potentials in JAX.
  ☆74Updated 3 months ago
• felixmusil / torch_nl
  ☆18Updated 2 years ago
• lab-cosmo / torch-pme
  Particle-mesh based calculations of long-range interactions in PyTorch
  ☆53Updated last week
• mariogeiger / allegro-jax
  ☆22Updated 2 months ago
• Luthaf / vesin
  Compute neighbor lists for atomistic systems
  ☆57Updated this week
• lab-cosmo / jax-pme
  Particle-mesh based calculations of long-range interactions in JAX
  ☆17Updated 5 months ago
• mariogeiger / nequip-jax
  ☆23Updated last year
• ilyes319 / mace-tutorials
  Collection of tutorials to use the MACE machine learning force field.
  ☆47Updated 10 months ago
• metatensor / featomic
  Computing representations for atomistic machine learning
  ☆72Updated this week
• balintmate / neural-thermodynamic-integration
  ☆11Updated 5 months ago
• jla-gardner / graph-pes
  train and use graph-based ML models of potential energy surfaces
  ☆99Updated last week
• microsoft / oneqmc
  Pretrained model for molecular wavefunctions
  ☆35Updated last week
• graphcore-research / mess
  MESS: Modern Electronic Structure Simulations
  ☆20Updated 10 months ago
• basf / mlipx
  Machine-Learned Interatomic Potential eXploration (mlipx) is designed at BASF for evaluating machine-learned interatomic potentials (MLIP…
  ☆89Updated last month
• ChengUCB / les
  ☆23Updated last month
• imagdau / Tutorials
  Collection of Tutorials on Machine Learning Interatomic Potentials
  ☆19Updated last year
• thorben-frank / mlff
  Build neural networks for machine learning force fields with JAX
  ☆123Updated 2 months ago
• 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
• ASK-Berkeley / StABlE-Training
  [TMLR 2025] Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
  ☆14Updated 5 months ago
• jla-gardner / load-atoms
  ⚛ download and manipulate atomistic datasets
  ☆46Updated 7 months ago
• AIforGreatGood / charge3net
  Higher-order equivariant neural networks for charge density prediction in materials
  ☆59Updated 5 months ago
• imagdau / aseMolec
  ☆28Updated 2 weeks ago
• tummfm / chemtrain
  Training Neural Network potentials through customizable routines in JAX.
  ☆41Updated this week
• nickjbrowning / cuda_mace
  CUDA implementations of MACE models
  ☆15Updated 6 months ago
• libAtoms / workflow
  python workflow toolkit
  ☆40Updated 2 weeks ago
• ASK-Berkeley / EScAIP
  [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential
  ☆52Updated 4 months ago
• tummfm / difftre
  Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
  ☆32Updated last year
• metatensor / metatrain
  Training and evaluating machine learning models for atomistic systems.
  ☆36Updated this week
• ASK-Berkeley / MLFF-distill
  [ICLR 2025] Official Implementation of "Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy …
  ☆18Updated 3 months ago
• whitead / symd
  N-Dimensional MD engine with symmetry group constraints written in C
  ☆49Updated last year