Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface
Jin Qian, Asmita Jana, Siddarth Menon, Andrew Bogdan, Rebecca Hamlyn, Johannes Mahl, Ethan J. Crumlin
Abstract
Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag–H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner. Interpreting spectroscopic data in real time remains a challenge in chemical characterization. Here a digital twin framework is developed that links first-principles theory and experimental data via a bidirectional feedback loop, enabling on-the-fly decision-making and insights into reaction mechanisms based on measured spectra during chemical experiments.