Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization
Open accessAbstract
Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large “compositional” space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10%) needed to complete an exhaustive search. We find an optimal composition within 15 ± 10 iterations in a HOIP compositional space containing 72 combinations, and within 31 ± 9 iterations when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.
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- Authors
- Herbol, H. C., Hu, W., Frazier, P., Clancy, P., & Poloczek, M.
- Affiliations
- Cornell University (US)
- University of Arizona (US)
- Corresponding author
- Poloczek, Matthias
- Venue
- npj Computational Materials
- Published
- 2018-09-10
- Volume
- 4
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2890019192
- Cited by
- 121
- References
- 47
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 14 · 2023: 15 · 2022: 23 · 2021: 23 · 2020: 14 · 2019: 9 · 2018: 1
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (1719875, 1120296, 1254298, 1536895)
- Cornell Center for Materials Research (DMR‐1719875, DMR‐1120296, 1120296, 1719875)
- Materials Research Science and Engineering Center, Harvard University (DMR- 1719875, DMR 1120296, 1719875)
- Division of Materials Research (DMR-1120296, DMR-1719875, 1120296, 1719875)
- Division of Civil, Mechanical and Manufacturing Innovation (CMMI-1536895, CMMI-1254298, DMR-1719875)
- Air Force Office of Scientific Research (FA9550-, FA9550‐15‐1‐0038, -15-1-, DMR-1120296)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Perovskite Materials and Applications
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- organic-inorganic hybrid perovskites, Bayesian optimization, solution-processed thin films, intermolecular binding energy, mixed halide, materials discovery, photovoltaic materials
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