Paulette Clancy

There are many problems at the forefront of materials chemistry that are stymied by their inherent complexity. Such problems are characterized by a rich landscape of parameters and processing variables that is combinatorially too large for either an experimental or a computational approach to solve through an exhaustive search. In such cases, the usual approach is an Edisonian trial-and-error approach, which inevitably leaves areas of parameter space largely or wholly unexplored.  The problems that we have explored are also characterized by a scarcity of data, since the data are expensive to acquire, both experimentally and computationally. This makes it an ideal candidate to solve using a Bayesian optimization (BayesOpt) approach, which provides a strategy for a global optimization of “black box” functions lacking a functional form. 

For much of a decade, we have used a Bayesian optimization approach to study the solution processing of metal halide perovskites, a promising class of materials for solar cell development. Solution processing offers a low-energy-use and deceptively simple protocol to create electronically active thin films with high solar cell efficiency. In this talk, we will cover our accomplishments, challenges and outlook for what Bayesian optimization might achieve to help us understand, and hence control, these processes.  I will end with some ideas of where we are taking BayesOpt in terms of having the ability to model nucleation and growth of metal halide perovskites and the algorithm development we will need to get us there.

Paulette Clancy, the Edward J. Schaefer Professor of chemical and biomolecular engineering, is the director of research for the AI-X Foundry, associate director of the Johns Hopkins Center for Integrated Structure-Mechanical Modeling and Simulation (CISMMS), and a fellow of the Hopkins Extreme Materials Institute (HEMI) and AIChE. She spent over 30 years teaching at Cornell before moving to Johns Hopkins in 2018 to become the inaugural department Head of ChemBE.

Clancy leads one of the top groups in the country studying atomic- and molecular-scale modeling of semiconductor materials, ranging from traditional silicon-based compounds to all-organic materials. Her group’s research comprises four main areas: advanced organic materials (covalent organic frameworks, antibacterial oligomers, organic electronics); algorithm development (force field development, machine learning, and Bayesian optimization); electronic materials (particularly III-IV semiconducting materials; and nucleation and crystal growth (hybrid organic/inorganic perovskites and quantum dot nanocrystals). Her lab focuses on studies of advanced materials processing and nucleation, including understanding the links between processing, structure, and function.

Her group is at the forefront of developing new Bayesian optimization methods to encode expert knowledge and intuition, creating optimal conditions for making energy-efficient solar cells, close-to-perfect quantum dots, and discovering polymorphs of electronic materials for shape memory applications.

She is a fierce long-term advocate for the increased representation of women and those from groups under-represented in engineering and the physical sciences.  She was the founding chair of a faculty group, “Women in Science and Engineering” for Cornell University. Among her awards for that advocacy are the American Institute of Chemical Engineers (AIChE) National Women’s Initiatives Mentoring Award. She is a member of the mentoring team for Project Elevate, a DEI initiative of Hopkins in partnership with NYU and CMU.

• Time: 11:00 AM
• Location: 206 Furnas Hall