Bias In, Physics Out - Ab-Initio Insights into Electrified Interfaces

Explore ab-initio computational approaches to understanding electrified solid-liquid interfaces in this 53-minute conference talk from IPAM's Embracing Stochasticity in Electrochemical Modeling Workshop. Delve into the central challenge of embedding applied electrode potential and electrical double layer charging into ab-initio simulations, examining both rigorous variable-charge, constant-potential methods and pragmatic surface-science-inspired strategies using coarse-grained or implicit electrolyte descriptions. Learn how these computational models predict potential- and pH-dependent surface compositions, simulate cyclic voltammograms, and estimate electrochemical reaction barriers under bias, while understanding their successes and limitations in extracting observables like exchanged charge. Discover the critical importance of explicit liquid structure and dynamics through recent work on Pt(111) capacitance in water, demonstrating why reproducing experimental trends requires ab-initio accuracy at applied bias. Gain insights into RAZOR, a novel response-augmented machine-learning potential that incorporates first- and second-order energy responses to bias through learned work-function and local Born-effective-charge information, enabling large-scale molecular dynamics at finite bias charge and bridging constant-potential physics with the system sizes and timescales necessary for interrogating interfacial structure, kinetics, and spectroscopy in electrocatalysis, corrosion, and energy storage applications.