aiqbench analysis
This study presents a closed-loop framework that combines evolutionary search with uncertainty-aware deep kernel learning to discover novel processing protocols for materials like ferroelectric thin films. By treating waveform optimization as an out-of-distribution discovery problem, the method efficiently identifies sequences that enhance material nonlinearity without relying on conservative interpolation or excessive measurements. The approach offers a scalable pathway for automating high-dimensional control tasks in synthesis and battery formation using AI-driven experimental validation.
Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning
arXiv:2606.13859v1 Announce Type: cross 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.
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.
Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
arXiv:2606.13859 [cond-mat.mtrl-sci]
(or arXiv:2606.13859v1 [cond-mat.mtrl-sci] for this version)
https://doi.org/10.48550/arXiv.2606.13859 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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