Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169079
Title: Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
Authors: Hippalgaonkar, Kedar
Li, Qianxiao
Wang, Xiaonan
Fisher, John W.
Kirkpatrick, James
Buonassisi, Tonio
Keywords: Engineering::Materials
Issue Date: 2023
Source: Hippalgaonkar, K., Li, Q., Wang, X., Fisher, J. W., Kirkpatrick, J. & Buonassisi, T. (2023). Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics. Nature Reviews Materials, 8(4), 241-260. https://dx.doi.org/10.1038/s41578-022-00513-1
Journal: Nature Reviews Materials
Abstract: As materials researchers increasingly embrace machine-learning (ML) methods, it is natural to wonder what lessons can be learned from other fields undergoing similar developments. In this Review, we comparatively assess the evolution of applied ML in materials research, gameplaying and robotics. We observe ML being integrated into each field in three phases: first into discrete hardware and software tools (toolset integration); second across different steps in a workflow (workflow integration); and third through the incorporation, generation and representation of generalizable knowledge beyond any one study (knowledge integration). We identify transferrable lessons from gameplaying and robotics to materials research, including adaptive and accessible automation, the gamification of grand challenges to focus community efforts on specific workflow integrations and motivate benchmarks and canonical datasets, and the adoption of hybrid (data-based and model-based) algorithms that combine domain expertise and current learning to economically address high-complexity tasks. We identify opportunities for researchers from different fields to collaborate, including novel ways to represent and integrate a rich but heterogeneous corpus of knowledge (such as heuristics, physical laws, literature or data) with ML algorithms to create new knowledge, and safe and equitable deployment of technologies with societally beneficial outcomes.
URI: https://hdl.handle.net/10356/169079
ISSN: 2058-8437
DOI: 10.1038/s41578-022-00513-1
Schools: School of Materials Science and Engineering 
Organisations: Institute of Materials Research and Engineering, A*STAR
Rights: © 2023 Springer Nature Limited. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MSE Journal Articles

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