The Open Catalyst Challenge 2021: Competition Report.

Annual Conference on Neural Information Processing Systems(2021)

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摘要
In this report, we describe the Open Catalyst Challenge held at NeurIPS 2021, focusing on using machine learning (ML) to accelerate the search for low-cost catalysts that can drive reactions converting renewable energy to storable forms. Specifically, the challenge required participants to develop ML approaches for relaxed energy prediction, i.e. given atomic positions for an adsorbate-catalyst system, the goal was to predict the energy of the system’s relaxed or lowest energy state. To perform well on this task, ML approaches need to approximate the quantum mechanical computations in Density Functional Theory (DFT). By modeling these accurately, the catalyst’s impact on the overall rate of a chemical reaction may be estimated; a key factor in filtering potential electrocatalyst materials. The challenge encouraged community-wide progress on this task and the winning approach improved direct relaxed energy prediction by 15% relative over the previous state-of-the-art.
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