Comparative analysis of search approaches to discover donor molecules for organic solar cells
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Published version
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Author(s)
Type
Journal Article
Abstract
Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over 10¹4 molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.
Date Issued
2025-10-01
Date Acceptance
2025-08-12
Citation
Digital Discovery, 2025, 4 (10), pp.2781-2796
ISSN
2635-098X
Publisher
Royal Society of Chemistry
Start Page
2781
End Page
2796
Journal / Book Title
Digital Discovery
Volume
4
Issue
10
Copyright Statement
© 2025 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.
License URL
Identifier
10.1039/d4dd00355a
Publication Status
Published
Date Publish Online
2025-08-13