Multirole collaborative and co-constructive materials design ecosystem enabled by using control and data flows decoupled workflows

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Authors: Bing He, Zhou Jiang, Kaixuan Wang, Qisen Wang, Zhicong Lai, Yueyu Zhang, Maxim Avdeev, Siqi Shi
Cited by: 8
References: 46

Abstract

Data-driven approaches are attracting wide attention in the field of materials science due to their capacity to unravel complex structure-activity relationships deriving from nonlinear interplay of materials properties across multiple scales. However, unlocking their potential in materials discovery and design requires addressing two main challenges: multi-disciplinary knowledge barriers across the entire materials data lifecycle (acquisition, processing, and analysis), and the absence of an infrastructure that can accommodate the continuous proliferation of data volume, algorithms, and models. Here, we propose a multirole collaborative and co-constructive materials design ecosystem that restructures both the productive forces and the relations of production in materials design. By establishing a structured division of labor and a customized materials design infrastructure with a workflow system that decouples control and data flows, our framework reduces inter-module dependencies and enables the flexible, scalable integration of heterogeneous resources. A case study on electrochemical storage materials design demonstrates that this approach can improve streamlined collaborative efficiency by at least 50%, highlighting its potential to accelerate materials design. This work establishes a new paradigm for building intelligent materials design platforms, characterized by dynamic composability instead of static integration, thereby fostering an open and sustainable ecosystem for future materials discovery. • Proposing a loosely coupled multirole collaborative and co-constructive materials design ecosystem. • Designing a workflow system that decouples data and control flows. • Providing a co-constructive infrastructure prototype for materials design. • Supporting coordinated service and independent scaling of heterogeneous research resources.

Fields of Study & Subjects

Machine Learning in Materials Science Scientific Computing and Data Management CO2 Reduction Techniques and Catalysts

Publication details

Journal / source
Journal of Materiomics
Publisher
Elsevier BV
Volume
12
Issue
2
Pages
101177–101177
Publication date
23 January 2026
Language
EN