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We present the Open MatSci ML Toolkit: a flexible, self-contained, andscalable Python-based framework to apply deep learning models and methods onscientific data with a specific focus on materials science and the OpenCatalystDataset. Our toolkit provides: 1. A scalable machine learning workflow formaterials science leveraging PyTorch Lightning, which enables seamless scalingacross different computation capabilities (laptop, server, cluster) andhardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support forrapid graph neural network prototyping and development. By publishing andsharing this toolkit with the research community via open-source release, wehope to: 1. Lower the entry barrier for new machine learning researchers andpractitioners that want to get started with the OpenCatalyst dataset, whichpresently comprises the largest computational materials science dataset. 2.Enable the scientific community to apply advanced machine learning tools tohigh-impact scientific challenges, such as modeling of materials behavior forclean energy applications. We demonstrate the capabilities of our framework byenabling three new equivariant neural network models for multiple OpenCatalysttasks and arrive at promising results for compute scaling and modelperformance.

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