Thanks to the rapid advances in artificial intelligence, AI for science (AI4Science) has emerged as one of the new promising research directions for modern science and engineering. In this review, we focus on recent efforts to develop knowledge-driven Bayesian learning and experimental design methods for accelerating the discovery of novel functional materials as well as enhancing the understanding of composition-process-structure-property relationships. We specifically discuss the challenges and opportunities in integrating prior scientific knowledge and physics principles with AI and machine learning (ML) models for accelerating materials and knowledge discovery. The current state-of-the-art methods in knowledge-based prior construction, model fusion, uncertainty quantification, optimal experimental design, and symbolic regression are detailed in the review, along with several detailed case studies and results in materials discovery.
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Systems Approaches to Materials Design: Past, Present, and Future
There is increasing awareness of the imperative to accelerate materials discovery, design, development, and deployment. Materials design is essentially a goal-oriented activity that views the material as a complex system of interacting subsystems with models and experiments at multiple scales of materials structure hierarchy. The goal of materials design is effectively to invert quantitative relationships between process path, structure, and materials properties or responses to identify feasible materials. We first briefly discuss challenges in framing process-structure-property relationships for materials and the critical role of quantifying uncertainty and tracking its propagation through analysis and design. A case study exploiting inductive design of ultrahigh-performance concrete is briefly presented. We focus on important recent directions and key scientific challenges regarding the highly collaborative intersections of materials design with systems engineering, uncertainty quantification and management, optimization, and materials data science and informatics, which are essential to fueling continued progress in systems-based materials design.
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- Award ID(s):
- 1663130
- NSF-PAR ID:
- 10150044
- Date Published:
- Journal Name:
- Annual Review of Materials Research
- Volume:
- 49
- Issue:
- 1
- ISSN:
- 1531-7331
- Page Range / eLocation ID:
- 103 to 126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Design/Method This qualitative, embedded multiple case study considered in‐depth pre‐ and post‐year interviews with teachers, administrators, industry, and university personnel during the first year of the Partnering with Educators and Engineers in Rural Schools (PEERS) program. Transcripts were analyzed using a framework of interorganizational collaboration operationalized for our context.
Results Results provide insights into stakeholder perceptions of collaborative processes in the first year of the PEERS program across dimensions of collaboration. These dimensions mapped to three central discussion points with relevance for school–university–industry partnerships: school collaboration as an emergent and negotiated process, tension in collaborating across organizations, and fair share in collaborating toward a social goal.
Conclusions Taking a macro‐level look at the collaborative processes involved enabled us to develop implications for collaborative stakeholders to be intentional about designing for future success. By systematically applying a framework of collaboration and capitalizing on the rich situational findings possible through a qualitative approach, we shift our understanding of collaborative processes in school–university–industry partnerships for engineering education and contribute to the development of collaboration theory.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-matsci-070218-125955
