mlsanityData to 7 Oct 2026

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

arXiv 2210.17484 ↗First posted 31 Oct 2022cs.LG cond-mat.mtrl-sci cs.AIcited by 14

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Abstract (arXiv)

We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which enables seamless scaling across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for rapid graph neural network prototyping and development. By publishing and sharing this toolkit with the research community via open-source release, we hope to: 1. Lower the entry barrier for new machine learning researchers and practitioners that want to get started with the OpenCatalyst dataset, which presently comprises the largest computational materials science dataset. 2. Enable the scientific community to apply advanced machine learning tools to high-impact scientific challenges, such as modeling of materials behavior for clean energy applications. We demonstrate the capabilities of our framework by enabling three new equivariant neural network models for multiple OpenCatalyst tasks and arrive at promising results for compute scaling and model performance.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 27 of its 48 references that are in the map. First posted on arXiv 31 Oct 2022.

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27 of 48 references.

References · 27

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2018How Powerful are Graph Neural Networks?cited by 4,355
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  6. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  7. 2021E(n) Equivariant Graph Neural Networkscited by 951
  8. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  9. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  10. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  11. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  12. 2020Directional Message Passing for Molecular Graphscited by 556
15 more
  1. 2021GemNet: Universal Directional Graph Neural Networks for Moleculescited by 342
  2. 2019Cormorant: Covariant Molecular Neural Networkscited by 262
  3. 2022Torsional Diffusion for Molecular Conformer Generationcited by 262
  4. 2020Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Moleculescited by 254
  5. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 254
  6. 2020The Open Catalyst 2020 (OC20) Dataset and Community Challengescited by 246
  7. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 106
  8. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  9. 2022On scientific understanding with artificial intelligencecited by 68
  10. 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
  11. 2022Towards Training Billion Parameter Graph Neural Networks for Atomic Simulationscited by 25
  12. 2021Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphscited by 18
  13. 2021Geometric Algebra Attention Networks for Small Point Cloudscited by 10
  14. 2022Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atomscited by 5
  15. 2020Deep Graph Library Optimizations for Intel(R) x86 Architecturecited by 3

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Citing papers per year: 2022: 1; 2023: 6; 2024: 3; 2025: 4.

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  1. 2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
  2. 2023A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systemscited by 65
  3. 2025SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Modelscited by 48
  4. 2024Foundational Large Language Models for Materials Researchcited by 18
  5. 2023EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulationscited by 15
  6. 2023MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modelingcited by 14
  7. 2023Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networkscited by 10
  8. 2024Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNNcited by 8
  9. 2025A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Toolscited by 7
  10. 2022PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Designcited by 6
  11. 2025UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurementscited by 5
  12. 2025CrystalGym: A New Benchmark for Materials Discovery Using Reinforcement Learningcited by 3
2 more
  1. 2024Deconstructing equivariant representations in molecular systemscited by 2
  2. 2023On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictionscited by 1