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Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret · Kin Long Kelvin Lee · Carmelo Gonzales · Marcel Nassar · Krzysztof Sadowski

Fri Dec 02 08:30 AM -- 08:40 AM (PST) @
in AI for Accelerated Materials Design (AI4Mat) »
Event URL: https://openreview.net/forum?id=iZ_J4vzeaO- »

The Open MatSci ML Toolkit is 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. The primary components of our toolkit include: 1.Scalable computation of experiments leveraging PyTorch Lightning across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU) without sacrificing performance in the compute and modeling; 2. Support for DGL for rapid graph neural network development. By sharing this toolkit with the research community via open-source release, we aim to: 1. Ease of use for new machine learning researchers and practitioners that want get started on interacting with the OpenCatalyst dataset which currently makes up 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 climate change applications.

Keywords: graph neural networks · materials science · OpenCatalyst Dataset · Software Frameworks

Author Information

Santiago Miret (Intel AI Lab)
Kin Long Kelvin Lee (Intel Corporation)
Carmelo Gonzales (Intel)
Marcel Nassar (Intel)
Krzysztof Sadowski

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