Chemical Engineering Seminar

Title: Ab initio-driven machine-learning models for aqueous systems and molten salts

Abstract: This presentation focuses on recently developed machine-learning models based on ab initio quantum chemical methods. These allow simulations of aqueous systems and molten salts over time and length scales previously reachable only for classical (empirical) force fields. In particular, we take advantage of the Deep Potential (DeePMD) methodology to capture energies and forces in a given system, as determined from quantum density functional theory. The DeePMD approach has the advantage that a well-developed software framework is available for rapid training of the relevant models and efficient implementation within standard open-source molecular dynamics codes. Example applications are presented here for water's vapor-liquid and liquid-liquid phase behavior, fluid-phase properties of CO2, and properties of aqueous electrolyte solutions that are hard to obtain accurately using empirical force fields. In addition, we investigate rates for bubble cavitation and homogeneous ice nucleation. Further illustrations of the power of the DeePMD approach are provided by modeling carbonate and hydroxide melts relevant for high-temperature fuel cell operations. Ab initio-based machine learning models are shown to have excellent predictive capabilities for thermodynamic and transport properties of solutions and melts with no need for input from experimental data. They automatically include multibody, polarizability and charge transfer effects, and naturally describe chemical reactions. However, they are limited in accuracy by the underlying quantum chemical methods and have complex, physically opaque model structures. Despite these disadvantages, they likely represent the future of molecular modeling at the atomistic level of detail.

Bio: Athanassios Z. Panagiotopoulos received an undergraduate degree from the National Technical University of Athens in 1982 and a PhD from MIT in 1986, both in Chemical Engineering. He was a postdoctoral fellow in Physical Chemistry at the University of Oxford (1986-87) and held faculty positions at Cornell (1987-97) and the University of Maryland (1997-2000). He is currently the Susan Dod Brown Professor of Chemical and Biological Engineering at Princeton University, where he also served as Department Chair from 2016-2022. He has received numerous honors, including the AIChE AXΣ and Colburn Awards, the FOMMS medal, and the Prausnitz Award in applied chemical thermodynamics. He was elected to the U.S. National Academy of Engineering in 2004 and to the American Academy of Arts and Sciences in 2012. He is the author of more than 350 technical papers and of the undergraduate textbook "Essential Thermodynamics" (2nd Ed., 2026).