Alternatives and similar repositories for phonondos_e3nn

Users that are interested in phonondos_e3nn are comparing it to the libraries listed below

• ulissigroup / finetuna
  Active Learning for Machine Learning Potentials
  ☆59Updated 2 months ago
• ftherrien / p2ptrans
  An algorithm to match crystal structures atom-to-atom
  ☆53Updated 2 years ago
• OUnke / SpookyNet
  Reference implementation of "SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects"
  ☆79Updated 3 years ago
• gomes-lab / Mat2Spec
  ☆29Updated 3 years ago
• lanl / ALF
  A framework for performing active learning for training machine-learned interatomic potentials.
  ☆39Updated last month
• ulissigroup / amptorch
  AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch
  ☆61Updated 2 years ago
• autoatml / autoplex
  Code for automated fitting of machine learned interatomic potentials.
  ☆126Updated 3 weeks ago
• sirmarcel / cmlkit
  tools for machine learning in condensed matter physics and quantum chemistry
  ☆33Updated 3 years ago
• ninarina12 / phononDoS_tutorial
  Code repository for a tutorial based on the "Direct prediction of phonon density of states with Euclidean neural networks"
  ☆30Updated last year
• zishengz / gocia
  Global Optimizer for Clusters, Interfaces, and Adsorbates
  ☆28Updated 7 months ago
• richardjgowers / zeoplusplus
  Mirror of http://zeoplusplus.org/
  ☆11Updated 7 years ago
• usnistgov / jarvis_leaderboard
  A large scale benchmark of materials design methods: https://www.nature.com/articles/s41524-024-01259-w
  ☆71Updated 2 months ago
• ICAMS / calphy
  A Python library and command line interface for automated free energy calculations
  ☆84Updated 2 weeks ago
• WMD-group / ElementEmbeddings
  Python package to interact with high-dimensional representations of the chemical elements
  ☆46Updated last week
• atomicarchitects / phonax
  ☆21Updated last year
• lrcfmd / ChemDASH
  Chemically Directed Atom Swap Hopping -- Crystal structure prediction by swapping atoms in unfavourable chemical environments
  ☆22Updated 2 years ago
• CederGroupHub / smol
  Statistical Mechanics on Lattices
  ☆89Updated last week
• argonne-lcf / active-learning-md
  Active learning workflow developed as a part of the upcoming article "Machine Learning Inter-Atomic Potentials Generation Driven by Activ…
  ☆28Updated 4 years 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
• hsulab / GDPy
  Generating Deep Potential with Python
  ☆70Updated last week
• CSIprinceton / workshop-july-2022
  Deep Modeling for Molecular Simulation, two-day virtual workshop, July 7-8, 2022
  ☆53Updated 3 years ago
• MaterSim / PyXtal_FF
  Machine Learning Interatomic Potential Predictions
  ☆94Updated last year
• sparks-baird / matbench-genmetrics
  Generative materials benchmarking metrics, inspired by guacamol and CDVAE.
  ☆40Updated last year
• aronwalsh / MLforMaterials
  Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)
  ☆97Updated 2 weeks ago
• CompRhys / aviary
  The Wren sits on its Roost in the Aviary.
  ☆60Updated 3 weeks ago
• mir-group / pair_nequip_allegro
  LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials
  ☆54Updated last month
• dogusariturk / PhaseForge
  PhaseForge is a framework for high-throughput alloy phase diagram prediction using machine learning interatomic potentials (MLIPs) integr…
  ☆48Updated last month
• ICAMS / grace-tensorpotential
  GRACE models and gracemaker (as implemented in TensorPotential package)
  ☆74Updated this week
• ilyes319 / mace-tutorials
  Collection of tutorials to use the MACE machine learning force field.
  ☆48Updated last year
• Yangxinsix / curator
  A flexible workflow for on-the-fly learning of interatomic potential models.
  ☆31Updated last week