A Vision for the Future of Materials Innovation and How to Fast-Track It with Services

Lorenz J. Falling*, 
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Abstract

Today, we witness how our scientific ecosystem tries to accommodate a new form of intelligence, artificial intelligence (AI). To make the most of AI in materials science, we need to make the data from computational and laboratory experiments machine-readable, but while that works well for computational experiments, integrating laboratory hardware into a digital workflow seems to be a formidable barrier toward that goal. This paper explores measurement services as a way to lower this barrier. I envision the Entity for Multivariate Material Analysis (EMMA), a centralized service that offers measurement bundles tailored for common research needs. EMMA’s true strength, however, lies in its software ecosystem to treat, simulate, and store the measured data. Its close integration of measurements and their simulation not only produces metadata-rich experimental data but also provides a self-consistent framework that links the sample with a snapshot of its digital twin. If EMMA was to materialize, its database of experimental data connected to digital twins could serve as the fuel for physics-informed machine learning and a trustworthy horizon of expectations for material properties. This drives material innovation since knowing the statistics helps find the exceptional. This is the EMMA approach: fast-tracking material innovation by integrated measurement and software services.

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材料创新的未来愿景以及如何利用服务快速实现创新
今天,我们目睹了科学生态系统如何努力适应一种新的智能形式--人工智能(AI)。要在材料科学领域充分利用人工智能,我们需要让计算实验和实验室实验的数据具有机器可读性,虽然这对计算实验很有效,但将实验室硬件集成到数字工作流程中似乎是实现这一目标的巨大障碍。本文探讨了降低这一障碍的测量服务。我设想的多变量材料分析实体(EMMA)是一种集中式服务,可提供针对常见研究需求量身定制的测量捆绑服务。不过,EMMA 的真正优势在于其处理、模拟和存储测量数据的软件生态系统。它将测量与模拟紧密结合,不仅产生了元数据丰富的实验数据,还提供了一个自洽的框架,将样本与其数字孪生快照联系起来。如果EMMA得以实现,其与数字孪生连接的实验数据数据库将成为以物理学为基础的机器学习的燃料,并为材料特性提供一个值得信赖的预期范围。这将推动材料创新,因为了解统计数据有助于发现非凡之处。这就是 EMMA 方法:通过集成测量和软件服务快速跟踪材料创新。
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来源期刊
CiteScore
3.70
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0.00%
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期刊介绍: ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis
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