Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany.
The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, Berlin, Germany.
Nature. 2022 Apr;604(7907):635-642. doi: 10.1038/s41586-022-04501-x. Epub 2022 Apr 27.
The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.
我们社会的繁荣和生活方式在很大程度上受到凝聚态物理、化学和材料科学成就的支配，因为能源、环境、健康、移动性和信息技术 (IT) 等领域的新产品在很大程度上依赖于改进甚至新材料。例如，固态照明、触摸屏、电池、植入物、药物输送等等。这些领域每天产生的大量研究数据代表了 21 世纪的一座金矿。然而，如果这些数据没有得到全面的描述和提供，这些数据就没有什么价值。我们如何提炼这种原料；也就是说，将数据转化为知识和价值？为此，FAIR（可发现、可访问、可互操作和可重复使用）数据基础设施是必须的。只有这样，才能使用数据分析和人工智能 (AI) 方法轻松地共享和探索数据。使数据“可发现和 AI 就绪”（对首字母缩写词的前瞻性解释）将改变当今科学的开展方式。在本观点中，我们讨论了我们如何为材料科学领域做好准备来实现这一目标。
