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Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery
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DOI：10.1016/j.patter.2023.100863.png)
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摘要
En 中文
Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design.
Keyword:
PRIOR DISTRIBUTIONS
MODEL SELECTION
OBJECTIVE COST
INFORMATION
NETWORKS
FRAMEWORK
PRIORS
REGRESSION
SYSTEMS
NOISE
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7.4
论文数:
951
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3.6K
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