Biography
Peter S. Rice was born in Co. Antrim, N. Ireland in 1994. He received a MSci (grad. 2016) and Ph.D. (grad. 2020, supervisor: prof. Peijun Hu) from The Queen’s University of Belfast in Chemistry and Computational Chemistry respectively. In March 2020, he moved to Pacific Northwest National Laboratory (PNNL) as a postdoctoral research associate to work with Dr. Simone Raugei in the Center for Molecular Electrocatalysis – an Energy Frontier Research Center. In 2023 he was promoted to the position of staff scientist within the Physical Sciences Directorate at PNNL, working on topics broadly related to energy storage. In 2026 he joined the Computational Chemistry group in the van ‘t Hoff Institute for Molecular Sciences (HIMS) as a tenure-track Assistant Professor in Computational Nano-electrochemistry. His position is affiliated with the Advanced Nano-electrochemistry Institute of the Netherlands (ANION) consortium. His research focuses on studying nano- and meso-scale processes that drive the design of efficient platforms for hydrogen and renewable energy storage applications. Specifically, his work is centered on understanding how complex electrochemical phenomena regulates the rate and selectivity of catalytic pathways in the bulk and interfacial regions at solid-liquid interfaces. The computational tools developed are vital for the interpretation of experimental observations and to guide new experimental directions.
Education
- Ph.D. Computational and Theoretical Chemistry, The Queen’s University of Belfast, 2017-2020
- MSci Chemistry, The Queen’s University of Belfast, 2012-2016
Academic Appointments
- Assistant Professor, University of Amsterdam, 2026-present
- Staff Scientist, Pacific Northwest National Laboratory, 2023-2026
- Postdoctoral Research Associate, Pacific Northwest National Laboratory, 2020-2023
Research interests
Our group studies chemical transformations at electrochemical solid-liquid interfaces using atomistic computational methods and high-performance computing resources. Our approach uses a combination of molecular dynamics (MD), density functional theory (DFT) and machine learning (ML) methods. We have expertise in the following areas:
- Artificial Intelligence/Machine Learning
- Catalysis Science
- Electrocatalysis
- Electrochemistry
- Hydrogen Storage
- Interfaces
- Surface Science
- Materials Simulation
- Molecular Dynamics
- Reaction Mechanisms
- Redox Flow Batteries
- Solid State Chemistry