Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning
Organizations: Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA · Materials Science and Engineering Department, Materials Research Institute, the Pennsylvania State University, University Park, Pennsylvania 16802, USA · Department of Materials Science and NanoEngineering, Rice University, Houston, TX 77005, USA · Rice Advanced Materials Institute, Rice University, Houston, Texas 77005, USA · Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, California 94720, USA · Departments of Chemistry and Physics and Astronomy, Rice University, Houston, Texas 77005, USA · Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, USA
Abstract
Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence of fields, temperatures, or chemical potentials applied during operation. Discovering new processing protocols is therefore a high-dimensional search problem in which the control variable is an entire waveform or sample history, and conventional strategies either remain confined to conservative interpolative families or become prohibitively measurement intensive. Here, a closed-loop workflow is introduced that couples evolutionary search over a compact waveform representation with uncertainty-aware deep kernel learning to generate, rank, and experimentally validate candidate protocols. Applied to ferroelectric thin films, with the scanning-probe tip-bias waveform as the protocol and the nonlinear electromechanical response as the reward, the workflow discovers waveform families that enhance nonlinearity by de-aging the film. Spatially resolved before/after measurements show that the best-performing waveforms selectively activate pre-existing, weakly pinned domain-wall segments, whereas the worst drive long-range irreversible switching. This framework reframes protocol tuning as out-of-distribution discovery, generalizable to synthesis and annealing trajectories, battery formation protocols, and other high-dimensional control problems.