Machine Learning Guided Polymorph Selection in Molecular Beam Epitaxy of In2Se3

Abstract

Indium selenide (In2Se3), a layered chalcogenide with multiple polymorphs, is a promising material for optoelectronic and ferroelectric applications. However, achieving polymorph-pure thin films remains a major challenge due to the complex growth space. In this work, Bayesian optimization (BO) is successfully leveraged to guide the molecular beam epitaxy growth of In2Se3 on Al2O3 substrates. By training a predictive Gaussian process regressor with sequential learning, we efficiently explored substrate temperature, indium flux, selenium flux, and cracker temperature, reducing experimental trials required for successful synthesis. A γ-In2Se3 film with 91% phase purity was achieved in fewer than 10 BO run samples. Attempts to isolate α-In2Se3 were limited by amorphous film formation at low temperatures, indicating that single-step codeposition is unsuitable for crystalline α-In2Se3 on Al2O3. Overall, this study validates BO as a powerful approach for phase-selective growth in complex material systems.