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Workshop: AI for Accelerated Materials Design (AI4Mat)

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

Keywords: [ Software Frameworks ] [ materials science ] [ graph neural networks ] [ OpenCatalyst Dataset ]


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.

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