| Paulette Clancy | |
| Birth Place: | London |
| Thesis Title: | Molecular interactions |
| Thesis Year: | 1977 |
| Fields: | Chemical engineering |
| Thesis Url: | https://solo.bodleian.ox.ac.uk/permalink/44OXF_INST/35n82s/alma990206232810107026 |
| Alma Mater: | Queen Elizabeth College University of Oxford |
Paulette Clancy is a British physicist who is Professor of Materials Science at Johns Hopkins University. Her research investigates the development of machine learning strategies to advance innovation in materials design, with a focus on complex situations.
Clancy is from London.[1] [2] She was an undergraduate student at Queen Elizabeth College, where she studied chemistry. She moved to the University of Oxford for her doctoral research in physical chemistry.[3] [4]
Clancy was a postdoctoral researcher at both Cornell University and the University of London. While at Cornell, she bought a Chevrolet Camaro and drove all around North America. She returned to the United Kingdom, where she worked for an oil company.
In 1987, Clancy joined the faculty at Cornell University. She spent over thirty years at Cornell, eight as the Director of the Computational Science and Engineering and Bodman Chair of Chemical Engineering. Clancy founded the Women in Science community at Cornell, and has advocated for equity and inclusion throughout her career.[5] She joined the faculty at the Whiting School of Engineering in 2018, and was appointed the Edward J. Schaefer Professor in 2023.[6]
Clancy's research investigates the use of computational materials science. She looks to understand the relationship between atomic structure and material properties. At Johns Hopkins University, she leads the Data Science and AI Initiative. Her research involves computational simulations of semiconducting materials, include inorganic (e.g. silicon and germanium) and molecular systems.[7] She is interested in the simulation of complex organic systems (e.g. antibacterial oligomers and covalent organic frameworks), understanding processes involved during nucleation and crystal growth, and the development of large-scale models for materials innovation (machine learning, force fields, Bayesian optimisation).