Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules

[2602.22531] Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Materials Science arXiv:2602.22531 (cond-mat) [Submitted on 26 Feb 2026] Title: Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules Authors: Yuki K. Wakabayashi , Yui Ogawa , Franz Benedict Romero , Takuma Otsuka , Yoshitaka Taniyasu View a PDF of the paper titled Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules, by Yuki K. Wakabayashi and 4 other authors View PDF Abstract: Self-driving laboratories have emerged as powerful tools for navigating high-dimensional process spaces, yet systems remain black-box optimizers that yield limited transferable process understanding. Here, we demonstrate an interpretable self-driving laboratory framework that transforms autonomous optimization into human-usable growth rules. As a stringent benchmark, we apply this framework to RF magnetron sputtering, addressing a long-standing challenge of achieving high-quality beta-Ga2O3 heteroepitaxy and single-crystalline beta-Ga2O3 homoepitaxy via sputtering. By combining Bayesian optimization with automated optical evaluation of the Urbach energy as a metric of sub-bandgap disorder, the self-driving system efficiently identifies heteroepitaxial growth conditions yielding a minimum Urbach energy of 182 meV, the lowest value for sputtered beta-Ga2O3 films. Importantly, the optimized growth window is transferable, realizing single-crystalline beta-Ga2O3 homoepitaxy without further optimization, corroborated by scanning transmission electron microscopy. To convert the closed-loop dataset into interpretable growth rules, we train a random forest surrogate and distill it into response curves and quantified pairwise interactions across the four-dimensional growth-parameter space. This analysis identifies substrate temperature as the primary control knob, with RF power and gas flows acting largely additively and only a modest temperature-oxygen coupling delineating the narrow window for high-quality growth, establishing a general route from autonomous experimentation to transferable growth rules. Subjects: Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2602.22531 [cond-mat.mtrl-sci] (or arXiv:2602.22531v1 [cond-mat.mtrl-sci] for this version) https://doi.org/10.48550/arXiv.2602.22531 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1038/s41467-026-76533-0 Focus to learn more DOI(s) linking to related resources Submission history From: Yuki Wakabayashi [ view email ] [v1] Thu, 26 Feb 2026 02:10:26 UTC (3,093 KB) Full-text links: Access Paper: View a PDF of the paper titled Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules, by Yuki K. Wakabayashi and 4 other authors View PDF view license Current browse context: cond-mat.mtrl-sci < prev | next > new | recent | 2026-02 Change to browse by: cond-mat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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