论文标题
可持续能源未来的机器学习
Machine Learning for a Sustainable Energy Future
论文作者
论文摘要
从化石燃料过渡到可再生能源是一个至关重要的全球挑战。它需要在可再生能源的有效收获，存储，转换和管理的材料，设备和系统水平上的进步。全球研究人员已经开始合并机器学习（ML）技术，目的是加速这些进步。 ML技术利用数据中的统计趋势来构建用于预测材料特性的模型，候选结构的产生，过程的优化以及其他用途；结果，可以将它们纳入发现和开发管道中以加速进步。在这里，我们回顾了ML驱动的能源研究，概述当前和未来挑战的最新进展，并描述了最佳杠杆ML技术所需的内容。首先，我们概述了关键ML概念。然后，我们引入了一组关键绩效指标，以帮助比较不同ML加速工作流对能源研究的好处。我们讨论并评估将ML应用于能源收集（光伏），存储（电池），转换（电催化）和管理（智能电网）的最新进展。最后，我们提供了能源领域潜在的研究领域的前景，这些研究领域将从ML的应用中进一步受益。
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy. Researchers globally have begun incorporating machine learning (ML) techniques with the aim of accelerating these advances. ML technologies leverage statistical trends in data to build models for prediction of material properties, generation of candidate structures, optimization of processes, among other uses; as a result, they can be incorporated into discovery and development pipelines to accelerate progress. Here we review recent advances in ML-driven energy research, outline current and future challenges, and describe what is required moving forward to best lever ML techniques. To start, we give an overview of key ML concepts. We then introduce a set of key performance indicators to help compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis), and management (smart grids). Finally, we offer an outlook of potential research areas in the energy field that stand to further benefit from the application of ML.
