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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium: Machine Learning and Autonomous Researchers for Materials Discovery and Design
Presentation Title Design of Halide Perovskites via Physics-informed Machine-learning
Author(s) Shijing Sun
On-Site Speaker (Planned) Shijing Sun
Abstract Scope Improving the environmental stability of halide perovskites is a critical challenge in perovskite solar cell development.1 Despite the remarkable photovoltaic performances,2 methylammonium lead iodide (MAPbI3) is notorious for its heat and moisture instability.3 Intensive research have been put into composition engineering in the past several years, where cation substitutions, e.g. incorporating alkaline metal Cs and small organic ion e.g. formamidinium (FA) into the MAPbI3 lattice, are shown to be among the most effective stabilization strategies.4 However, identifying and optimizing mixed-ion perovskites for reliability in real-world climates is a very challenging task due to the vast composition possibilities and the lack of physics-informed guidance. In this talk I will discuss our recent progress incorporating DFT into a Bayesian optimization algorithm5 to direct the search for novel semiconductors. To effectively design solar materials that are stable under the industrial standard of 85 RH% and 85°C reliability test, we combined the strengths of theory-guided and data-guided methodologies with in situ degradation tests, enabling a “smart search” strategy in a multi-parameter space. We took both calculations and experimental data into our machine-learning decision-making step, which have led to a significant acceleration in the search process. Validation on new materials are further achieved by an employment of the synchrotron-based high-throughput XRD measurement, where the degradation profiles are directly correlated to the underlying structural changes. This work sheds light on combining theory, machine-learning and high-throughput experimentation to accelerate the development of novel solar materials.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Adaptive Machine Learning for Efficient Navigation of Materials Space
Application of Machine Learning and Federated Big Data Storage & Analytics for Accelerated Additive Process and Parameter Development
Autonomous Research Systems for Materials Development
Autonomous Systems for Alloy Design: Towards Robust Closed-loop Alloy Deposition and Characterization
Bayesian Methods for Concrete Creep Prediction and Learning Optimized Concrete Microstructure Design
Closing the Loop in Autonomous Materials Development
Combining Simulation and Autonomous Experimentation for Mechanical Design
Design of Halide Perovskites via Physics-informed Machine-learning
Turning Statistical Mechanics Models into Materials Design Engines
Unraveling Hierarchical Materials using Autonomous Research Systems

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