Alternatives and similar repositories for MatDeepLearn_dev

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

• Eipgen / Neural-Network-Models-for-Chemistry
  A collection of Neural Network Models for chemistry
  ☆173Updated last month
• jiaor17 / DiffCSP-PP
  [ICLR 2024] The implementation for the paper "Space Group Constrained Crystal Generation"
  ☆53Updated last week
• MDIL-SNU / SevenNet
  SevenNet - a graph neural network interatomic potential package supporting efficient multi-GPU parallel molecular dynamics simulations.
  ☆201Updated last week
• mir-group / pair_nequip_allegro
  LAMMPS pair styles for NequIP and Allegro deep learning interatomic potentials
  ☆58Updated 2 months ago
• blaiszik / awesome-matchem-datasets
  ☆261Updated last month
• xiaohang007 / SLICES
  SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT, SLICES-PLUS
  ☆126Updated last month
• AIforGreatGood / charge3net
  [npj Comp. Mat.] Higher-order equivariant neural networks for charge density prediction in materials
  ☆66Updated 9 months ago
• WanyuGroup / AI-for-Crystal-Materials
  AI for crystal materials
  ☆96Updated this week
• usnistgov / alignn
  Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ https://www.youtube.com/@dr_k_choudhary
  ☆288Updated 3 months ago
• Open-Catalyst-Project / Open-Catalyst-Dataset
  Workflow for creating and analyzing the Open Catalyst Dataset
  ☆120Updated 9 months ago
• jiaor17 / DiffCSP
  [NeurIPS 2023] The implementation for the paper "Crystal Structure Prediction by Joint Equivariant Diffusion"
  ☆134Updated 7 months ago
• materialsproject / matbench
  Matbench: Benchmarks for materials science property prediction
  ☆174Updated last year
• LLNL / graphite
  A repository for implementing graph network models based on atomic structures.
  ☆95Updated last year
• facebookresearch / JMP
  Code for “From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction”.
  ☆60Updated last year
• ACEsuit / mace-foundations
  MACE foundation models (MP, OMAT, Matpes)
  ☆167Updated last week
• sarpaykent / GotenNet
  [ICLR 2025] GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
  ☆55Updated last month
• ASK-Berkeley / EScAIP
  [NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential
  ☆56Updated 2 months ago
• lantunes / CrystaLLM
  A Large Language Model of the CIF format for Crystal Structure Generation
  ☆138Updated last month
• RyotaroOKabe / SCIGEN
  Structural constraint integration in a generative model for the discovery of quantum materials
  ☆24Updated last month
• CederGroupHub / chgnet
  Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
  ☆343Updated last month
• TUM-DAML / gemnet_pytorch
  GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)
  ☆213Updated 2 years ago
• janosh / matbench-discovery
  An evaluation framework for machine learning models simulating high-throughput materials discovery.
  ☆201Updated 2 weeks ago
• sbanik2 / CEGANN
  Crystal Edge Graph Attention Neural Network
  ☆23Updated last year
• Fung-Lab / MatDeepLearn
  MatDeepLearn, package for graph neural networks in materials chemistry
  ☆198Updated 2 years ago
• dhw059 / DenseGNN
  DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
  ☆26Updated 10 months ago
• ICAMS / grace-tensorpotential
  GRACE models and gracemaker (as implemented in TensorPotential package)
  ☆76Updated last week
• jla-gardner / graph-pes
  train and use graph-based ML models of potential energy surfaces
  ☆114Updated this week
• RishikeshMagar / OGCNN
  Crystal graph convolutional neural networks for predicting material properties.
  ☆34Updated 3 years ago
• BingqingCheng / cace
  ☆107Updated this week
• Tinystormjojo / geo-CGNN
  ☆33Updated last year