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This content will become publicly available on November 1, 2024

Title: Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery
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.  more » « less
Award ID(s):
1835690
NSF-PAR ID:
10476413
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Cell Press
Date Published:
Journal Name:
Patterns
Volume:
4
Issue:
11
ISSN:
2666-3899
Page Range / eLocation ID:
100863
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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