Rapid Data‐Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and er...

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Veröffentlicht in:Advanced materials (Weinheim) Jg. 35; H. 16; S. e2208772 - n/a
Hauptverfasser: Lampe, Carola, Kouroudis, Ioannis, Harth, Milan, Martin, Stefan, Gagliardi, Alessio, Urban, Alexander S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany Wiley Subscription Services, Inc 01.04.2023
Schlagworte:
Algorithms
Bayesian optimization
data‐efficient optimization
Dispersions
Functional materials
Gaussian processes
halide perovskites
Homogeneity
Machine learning
Material properties
Materials science
Monolayers
Nanocrystals
Optimization
Parameters
Perovskites
Photoluminescence
Platelets (materials)
photoluminescence
data-efficient optimization
nanocrystals
Gaussian processes
halide perovskites
Bayesian optimization
machine learning
ISSN:0935-9648, 1521-4095, 1521-4095
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Zusammenfassung:With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well‐known machine‐learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer‐thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2–6‐monolayer‐thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less‐explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions’ quality. Designing and optimizing novel, efficient nanomaterials for renewable energies is arduous; however, machine learning can support this process. Here, three machine‐learning algorithms are merged with Bayesian optimization to significantly improve halide perovskite nanoplatelets’ homogeneity and optical properties. Significantly, the algorithm fusion results in a data‐efficient pipeline, requiring an order of magnitude fewer syntheses than previous methods to function proficiently.
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ISSN:0935-9648
1521-4095
1521-4095
DOI:10.1002/adma.202208772