Conference Tools for 2020 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
Presentation Title	Machine Learning-directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-confined Regime
Author(s)	Erick Braham, Junsang Cho, Kristel Forlano, Raymundo Arroyave, Sarbajit Banerjee
On-Site Speaker (Planned)	Erick Braham
Abstract Scope	Utilizing the synthesis of two-dimensional CsPbBr₃ nanoplatelets as a model system, we demonstrate an efficient machine learning navigation of reaction space that allows for predictive control of layer thickness down to sub-monolayer dimensions. Support vector machine (SVM) classification and regression models are used to initially separate regions of the design space that yield quantum-confined nanoplatelets from regions yielding bulk particles and subsequently to predict the thickness of quantum-confined CsPbBr₃ nanoplatelets that can be accessed under specific reaction conditions. The SVM models are not only just predictive and efficient in sampling the available design space but also provide fundamental insight into the influence of molecular ligands in constraining the dimensions of nanocrystals. The results illustrate a quantitative approach for efficient navigation of reaction design space and pave the way to navigation of more elaborate landscapes beyond dimensional control spanning polymorphs, compositional variants, and surface chemistry.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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Machine Learning-directed Navigation of Synthetic Design Space: A Statistical Learning Approach to Controlling the Synthesis of Perovskite Halide Nanoplatelets in the Quantum-confined Regime

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