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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) @
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.

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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