[论文解读] An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

C. Lawrence Zitnick, Lowik Chanussot+15 more|arXiv (Cornell University)|Oct 14, 2020

Machine Learning in Materials Science被引用 35

一句话总结

本文介绍了如何利用机器学习近似密度泛函理论计算，以加速用于氢能储存和甲烷合成的电催化剂设计，借助 OC20 数据集。

ABSTRACT

Scalable and cost-effective solutions to renewable energy storage are essential to addressing the world's rising energy needs while reducing climate change. As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand. This may require the storage of power for hours, days, or months. One solution that offers the potential of scaling to nation-sized grids is the conversion of renewable energy to other fuels, such as hydrogen or methane. To be widely adopted, this process requires cost-effective solutions to running electrochemical reactions. An open challenge is finding low-cost electrocatalysts to drive these reactions at high rates. Through the use of quantum mechanical simulations (density functional theory), new catalyst structures can be tested and evaluated. Unfortunately, the high computational cost of these simulations limits the number of structures that may be tested. The use of machine learning may provide a method to efficiently approximate these calculations, leading to new approaches in finding effective electrocatalysts. In this paper, we provide an introduction to the challenges in finding suitable electrocatalysts, how machine learning may be applied to the problem, and the use of the Open Catalyst Project OC20 dataset for model training.

研究动机与目标

• 推动可扩展、具成本效益的可再生能源储存策略（HES 与甲烷合成）。
• 解释电催化剂在改善电化学反应和降低成本方面的作用。
• 介绍密度泛函理论的局限性以及对基于 ML 的近似方法的需求。
• 将 Open Catalyst Project OC20 数据集作为催化领域 ML 模型的基础。

提出的方法

• 描述电催化剂发现中的挑战以及 DFT 弛豫的相关性。
• 讨论 ML 模型如何从 DFT 数据中学习，以预测能量和几何结构。
• 介绍 OC20 数据集，作为训练用于吸附与弛豫任务的 ML 模型的资源。
• 概述在表面预测催化剂-吸附体相互作用方面的潜在 ML 模型方向。

实验结果

研究问题

• RQ1 ML 如何近似量子力学计算，以加速用于可再生能源储存的催化剂发现？
• RQ2 哪些数据集和任务适合训练 ML 模型以预测吸附能和放松几何？
• RQ3 ML 加速的催化剂设计对 HES（氢能储存）和甲烷合成的可行性可能产生怎样的影响？

主要发现

• ML 可能提供对 DFT 的高效近似，从而实现催化剂选项的大规模探索。
• 将 OC20 提议为用于训练与催化剂筛选相关的吸附和弛豫任务的模型的数据集。
• 高效的 ML 模型可能使得对数百万种催化剂-吸附体组合进行测试成为可能，而单靠完整的 DFT 难以实现。
• 讨论强调了 DFT 的高计算成本以及 ML 在加速催化剂发现中的价值。
• 本文将氢能储存和甲烷合成作为 ML 辅助电催化剂设计的关键应用领域。