Bayesian Conavigation of a Computational Physical Model and Atomic Force Microscopy Experiment to Autonomously Survey a Combinatorial Materials Library

Abstract

Building autonomous experiment workflows requires transcending data-driven surrogate models to incorporate and dynamically refine physical theory during exploration. Here we demonstrate the fully automated experimental realization of Bayesian conavigation─a framework in which an autonomous agent simultaneously runs a physical experiment and a computationally expensive physical model. Using an automated Atomic Force Microscopy (AFM) platform coupled to a kinetic Monte Carlo (kMC) model of thin-film growth, the system infers a set of effective bond energies for the (CrTaWV)x–Mo(1–x) pseudobinary combinatorial library, progressively adjusting the kMC parameters to decrease the epistemic disparity between simulation and experiment. This real-time theoretical refinement enables the kMC model to capture the behavior of the specific materials system and reveals the mechanistic role of heterobonding in governing surface diffusion. Together, these results establish conavigation as a general strategy for tightly integrating physical models with autonomous experimental platforms to produce interpretable and continually self-correcting theoretical modeling of complex materials systems.