Falling Lorenz J
School of Natural Sciences, Technical University Munich, 85748 Munich, Germany.
ACS Phys Chem Au. 2024 Jun 12;4(5):420-429. doi: 10.1021/acsphyschemau.4c00009. eCollection 2024 Sep 25.
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.
如今，我们见证了我们的科学生态系统如何尝试接纳一种新的智能形式——人工智能（AI）。为了在材料科学中充分利用人工智能，我们需要使计算实验和实验室实验的数据能够被机器读取，虽然这在计算实验中效果良好，但将实验室硬件集成到数字工作流程似乎是实现这一目标的巨大障碍。本文探讨了测量服务作为降低这一障碍的一种方式。我设想了多变量材料分析实体（EMMA），这是一种集中式服务，提供针对常见研究需求量身定制的测量包。然而，EMMA的真正优势在于其用于处理、模拟和存储测量数据的软件生态系统。它将测量及其模拟紧密集成，不仅产生了富含元数据的实验数据，还提供了一个自洽的框架，将样本与其数字孪生的快照联系起来。如果EMMA能够实现，其与数字孪生相连的实验数据库可以为基于物理的机器学习提供动力，并为材料特性提供可靠的预期范围。这推动了材料创新，因为了解统计数据有助于发现异常情况。这就是EMMA方法：通过集成测量和软件服务快速推动材料创新。
