Designing workflows for materials characterization
Designing workflows for materials characterization
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DOI：
10.1063/5.0169961
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发表时间：
2023-02
影响因子：
15
通讯作者：
S. Kalinin;M. Ziatdinov;M. Ahmadi;Ayana Ghosh;Kevin M. Roccapriore;Yongtao Liu;R. Vasudevan
中科院分区：
文献类型：
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作者：
S. Kalinin;M. Ziatdinov;M. Ahmadi;Ayana Ghosh;Kevin M. Roccapriore;Yongtao Liu;R. Vasudevan
实验科学是由合成，成像和功能表征的组合组织成不断发展的发现循环。新材料的合成通常遵循一系列表征步骤，旨在为优化提供反馈或发现基本机制。然而，合成和表征方法的顺序及其解释，或研究工作流程，传统上由人类直觉驱动，并且具有高度的领域特异性。在这里，我们探讨的科学工作流程的概念，出现在理论，表征和成像之间的接口。我们讨论的标准，这些工作流程可以构建多分辨率结构成像和功能表征的特殊情况下，作为更一般的材料合成工作流程的一部分。提供了理论实验工作流程的一些考虑因素。我们进一步提出，用户设施和云实验室的出现破坏了工作流开发的构思，编排和执行阶段的经典进展。为了加速这种转变，我们提出了工作流设计的框架，包括通用超语言描述实验室操作，本体域匹配，奖励功能和域之间的集成，和工作流优化的政策制定。这些工具将使基于知识的工作流程优化;使横向仪器网络，顺序和并行编排不同设施之间的表征;并授权分布式研究。
Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. We discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.