DeCost B L, Hattrick-Simpers J R, Trautt Z, Kusne A G, Campo E, Green M L
National Institute of Standards and Technology, Gaithersburg, MD, USA.
National Science Foundation, Arlington, VA, USA.
Mach Learn Sci Technol. 2020;1(3). doi: 10.1088/2632-2153/ab9a20.
Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI (SciAI) to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical ( development of robust, physically meaningful multiscale material representations) to social ( promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.
最近，材料科学、凝聚态物理和化学领域使用机器学习（ML）和人工智能（AI）方法的趋势一直在不断增加。这篇观点文章确定了材料领域必须优先考虑的关键科学、技术和社会机遇，以便持续开发和利用科学人工智能（SciAI），为推动当前受材料限制的技术发展提供一条可靠的途径。在这里，我们突出这些机遇与一系列提议的前进道路的交叉点。这些机遇大致从科学/技术（开发强大的、具有物理意义的多尺度材料表示）到社会（培养适应人工智能的劳动力）进行排序。提议的前进道路包括从开发新的基础设施和能力到在工业界和学术界进行部署。我们首先简要介绍材料科学与工程中的人工智能，随后详细讨论每个机遇和前进道路。
