CAREER: Multiscale and Machine Learning Approaches for Electrified Interfaces

Dr. Oliviero Andreussi of the University of North Texas is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry and from the Condensed Matter and Materials Theory (CMMT) program in the Division of Materials Research. He will develop and apply new computational tools to the characterization of chemical processes at solid-liquid interfaces. The project combines hierarchical models and machine-learning techniques to provide accurate and inexpensive descriptions of aspects that control the operation of chemical devices, such as batteries, fuel cells, and sensing devices. The developed techniques are aimed at the systematic virtual screening of materials for electrocatalysis, starting from the emerging class of two-dimensional materials. The development of a computational mindset to address emerging technological problems represents the key educational component of the project. The educational component extends the use of computation to visualize science and to make it accessible and attractive to the public. Hackathon workshops will be adopted to engage younger researchers in computational thinking. The team will also use and develop visualization tools to expand the impact of the research to other fields and disciplines. Dr. Oliviero Andreussi is developing accurate and transferable approaches for modeling solid-liquid interfaces. To accomplish this goal, this project features an integrated research and education program focused on extending continuum models of electrochemical environments by embedding a first-principles description of materials. Dr Andreussi and his research group are pursuing new developments in hybrid multiscale approaches and machine-learning strategies of environment effects. The research improves the transferability and accuracy of simulations of wet and electrified interfaces. These new methods and techniques are applied to study the effects of complex embedding environments on the emerging class of two-dimensional (2D) materials. The developed computational tools allow a systematic screening of existing and proposed 2D materials to explore exfoliation strategies, to verify their stability in complex environments, to characterize their (electro-)catalytic activities, and to identify their role in sensing devices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.