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.

中文翻译：

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使用机器学习进行可再生能源存储的电催化剂设计简介

可扩展且具有成本效益的可再生能源存储解决方案对于解决世界不断增长的能源需求同时减少气候变化至关重要。随着我们越来越依赖风能和太阳能等可产生间歇性电力的可再生能源，需要存储将电力从发电高峰期转移到需求高峰期。这可能需要数小时、数天或数月的电力存储。一种可以扩展到国家级电网的解决方案是将可再生能源转化为其他燃料，例如氢气或甲烷。为了被广泛采用，该过程需要具有成本效益的解决方案来运行电化学反应。一个公开的挑战是寻找低成本的电催化剂以高速驱动这些反应。通过使用量子力学模拟（密度泛函理论），可以测试和评估新的催化剂结构。不幸的是，这些模拟的高计算成本限制了可以测试的结构数量。机器学习的使用可以提供一种有效地近似这些计算的方法，从而导致寻找有效电催化剂的新方法。在本文中，我们介绍了寻找合适的电催化剂的挑战、如何将机器学习应用于该问题，以及使用 Open Catalyst Project OC20 数据集进行模型训练。导致寻找有效电催化剂的新方法。在本文中，我们介绍了寻找合适的电催化剂的挑战、如何将机器学习应用于该问题，以及使用 Open Catalyst Project OC20 数据集进行模型训练。导致寻找有效电催化剂的新方法。在本文中，我们介绍了寻找合适的电催化剂的挑战、如何将机器学习应用于该问题，以及使用 Open Catalyst Project OC20 数据集进行模型训练。