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자연과학대학
물리학전공
Journal papers
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Race to the bottom: Bayesian optimisation for chemical problems
Title
Race to the bottom: Bayesian optimisation for chemical problems
Authors
Wu
;
Yifan
;
Walsh
;
Aron
;
Ganose
;
Alex M.
Ewha Authors
Aron Walsh
SCOPUS Author ID
Aron Walsh
Issue Date
2024
Journal Title
Digital Discovery
ISSN
2635-098X
Citation
Digital Discovery vol. 3, no. 6, pp. 1086 - 1100
Indexed
SCOPUS
Document Type
Review
Abstract
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application domain is that both the dimensionality of the problems and the cost of evaluations are high. The selection of an appropriate optimisation technique is key, with standard choices including iterative (e.g. steepest descent) and heuristic (e.g. simulated annealing) approaches, which are complemented by a new generation of statistical machine learning methods. We introduce Bayesian optimisation and highlight recent success cases in materials research. The challenges of using machine learning with automated research workflows that produce small and noisy data sets are discussed. Finally, we outline opportunities for developments in multi-objective and parallel algorithms for robust and efficient search strategies. © 2024 RSC.
DOI
10.1039/d3dd00234a
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