Experimental science is enabled by the combination of synthesis, imaging, and functional characterization. Synthesis of a new material is typically followed by a set of characterization methods 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 multi-resolution structural imaging and structural and functional characterization. Some considerations for theory-experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupt the classical progression from ideation, orchestration, and execution stages of workflow development and necessitate development of universal frameworks for workflow design, including universal hyper-languages describing laboratory operation, 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.
Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation
Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory（太平洋西北国家实验室物理与计算科学部门）；Department of Chemical Engineering, University of Washington（华盛顿大学化学工程系）
Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling
Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST（加泰罗尼亚纳米科学和纳米技术研究所 (ICN2)、西班牙国家研究委员会 (CSIC) 和巴塞罗那独立科技大学系统 (BIST)）；CNR Istituto Nanoscienze（意大利国家研究委员会纳米科学研究所）；University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）；Department of Physics and Astronomy “Ettore Majorana”, University of Catania（卡塔尼亚大学“埃托雷·马约拉纳”物理与天文学系）；CNR-IMM（意大利国家研究委员会微电子与微结构研究所 (CNR-IMM)）；Univ. Grenoble Alpes, CEA, IRIG-MEM-L Sim（格勒诺布尔阿尔卑斯大学、法国原子能和替代能源委员会 (CEA)、IRIG-MEM-L Sim）；PIC, IFAE（粒子物理国际中心 (PIC)、巴塞罗那高能物理研究所 (IFAE)）；Institute of Science and Technology Austria（奥地利科学技术研究所）；QuTech and Kavli Institute of Nanoscience, Delft University of Technology（代尔夫特理工大学 QuTech 和卡弗里纳米科学研究所）；NNF Quantum Computing Programme, Niels Bohr Institute, University of Copenhagen（哥本哈根大学尼尔斯·玻尔研究所 NNF 量子计算项目）；Laboratory for Epitaxial Nanostructures on Silicon and Spintronics, Physics Department, Politecnico di Milano（米兰理工学院物理系硅外延纳米结构与自旋电子学实验室）；Catalonia Institute for Energy Research – IREC（加泰罗尼亚能源研究所 (IREC)）；ICREA（加泰罗尼亚高级研究院 (ICREA)）
The Interoperability Challenge in DFT Workflows Across Implementations
Technical University of Denmark（丹麦技术大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Dassault Systèmes Germany GmbH（达索系统德国有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；SINTEF Industry（辛特夫工业）；Dassault Systèmes UK Limited（达索系统英国有限公司）；Paul Scherrer Institute（保罗谢勒研究所）
GEWUM: General Exploration Workflow for the Utopia of Materials: A Unified Platform for Automated Structure Generation, Selection, and Validation
Suzhou Laboratory（苏州实验室）；Research and Development Institute of Northwestern Polytechnical University in Shenzhen（深圳西北工业大学研究院）；School of Naval Architecture and Maritime, Zhejiang Ocean University（浙江海洋大学船舶与海事学院）；School of Physical Science and Technology, Northwestern Polytechnical University（西北工业大学物理科学与技术学院）
A Python workflow definition for computational materials design
Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；Bundesanstalt für Materialforschung und -prüfung（联邦材料研究与测试研究所）；Friedrich-Schiller Universität Jena（耶拿弗里德里希·席勒大学）；PSI Center for Scientific Computing, Theory and Data（瑞士保罗谢尔研究所科学计算、理论与数据中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Imperial College London（伦敦帝国理工学院）
Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Karlsruhe University of Applied Sciences (HKA)（卡尔斯鲁厄应用技术大学）
Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction
Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-Universität Bochum（波鸿鲁尔大学先进材料模拟跨学科中心）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；BAM Federal Institute for Materials Research and Testing（BAM联邦材料研究与测试研究所）
Complex Workflow Management and Integration of Distributed Computing Resources by Science Gateway Portal for Molecular Dynamics Simulations in Materials Science
Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization
1 Department of Computer Science, University of Chicago；2 Data Science \& Learning Division, Argonne National Laboratory；3 Department of Civil, Materials；Environmental Engineering, University of Illinois at Chicago
