Interpretable Machine Learning Uncovers the Dominance of Oxygen Partial Pressure and Crystallinity in Mo-Doped In2O3 Thin Film Growth.

Data-driven approaches have accelerated materials discovery, yet they often remain "black boxes" that prioritize performance over physical understanding. To bridge the gap between statistical correlation and physical causality, this study establishes an interpretable machine learning (IML) framework applied to the sputter deposition of Mo-doped In2O3 thin films. Unlike conventional predictive models, our approach uses XGBoost regression combined with feature-importance analysis to quantitatively decouple the entangled effects of deposition parameters. Crucially, the model autonomously discovered─without explicit prior knowledge─that the carrier density is governed by oxygen partial pressure (PO2), while electron mobility is driven by crystallinity depending on the trade-off effects between adatom diffusion and high-energy particle bombardment. We experimentally validated these ML-derived hypotheses, identifying that the PO2 dependence stems from defect compensation by interstitial oxygen rather than simple oxygen vacancies and that the mobility peak corresponds to optimal crystallinity. This work demonstrates that IML can effectively "rediscover" governing physical laws from small experimental data sets, offering a scalable strategy to elucidate growth mechanisms in functional materials.