Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session Q53: AI and Statistical/Thermal Physics
3:00 PM–6:00 PM,
Wednesday, March 8, 2023
Room: Room 307
Sponsoring
Unit:
GDS
Chair: Caroline Desgranges, University of Massachusetts Lowell
Abstract: Q53.00001 : Self-assembly of electronic materials and the power of machine learning
3:00 PM–3:36 PM
Presenter:
Paulette Clancy
(Johns Hopkins University)
Author:
Paulette Clancy
(Johns Hopkins University)
We have developed and used a Bayesian approach to study several such problems in materials chemistry. This talk will discuss the most mature test case in which we looked at “solvent engineering” to optimize the process by which some novel solar cells are fabricated. We developed a BayesOpt algorithm that is “physics-informed”
through its belief choice. We use that algorithm to study how to optimize the choice of solvent or additive to produce high quality thin films of lead-based hybrid organic-inorganic perovskites. These “perovskites” all exhibit a crystalline habit with the formula ABX3, where A and B are cations and X is an anion. Here, we chose B to be lead and X to be halides, and we allow A-site cations to be chosen among a small group of options. Using BayesOpt, we identified the best solvent out of a list of around 12 solvents, and also how to optimize the best mixed-halide system to solubilize the lead salt.
To validate these predictions, we used the Bayesian-generated ranked list of “best performers” to identify when a crystalline “intermediate” is likely to form, which can adversely affect thin film quality. We confirmed our ability to determine whether a precursor forms in six cases (three positive for crystalline intermediates and three negative for intermediates). To do so, we were fortunate to have XRD experiments performed by Lara Estroff’s group at Cornell that confirm our predictions.
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