Accelerated Development of Ga2O3 Thin-film Epitaxial Growth by AI Approaches with FAIR Data in NOMAD
Creators: name: Albino, Andrea
name: Chou, Ta-Shun
name: Näsström, Hampus
name: Kapoor, Sarthak
name: Golparvar, Amir
name: Chang, Theodore
name: Ladines, Alvin Noe
name: Himanen, Lauri
name: Nakhaee, Mohammad
name: Popp, Andreas
name: Marquez, José A.
name: Brückner, Sebastian
name: Scheidgen, Markus
name: Draxl, Claudia
name: Albrecht, Martin
Publisher: name: Zenodo
Year: 2024

description: Data-driven material science has the potential to transform the way we design and developmaterials. This emerging field represents a significant departure from traditional trial-and-error methods andempirical approaches that have characterized materials science for decades. Experiments in this area involvea multidimensional parameter space, making analysis challenging and time-consuming. Finding predictiveempirical relations that allow for precise control over various aspects of the synthesis process has posed achallenge to human cognitive abilities alone. This becomes even more complex when combining datasets fromdifferent labs or from different scientists due to the lack of established standards for data models and methodsto capture the large number of experimental details, including elaborate workflows and a large diversity ofinstruments for characterization. It requires a radical shift in how information is handled and research isperformed. Experiment data must be complemented with its rich-metadata context, also covering ontologiesand workflows according to the FAIR (findable, accessible, interoperable, reusable) principles [1].Opening new perspectives towards finding structure, correlations, and novel information with data-analysis andAI tools, therefore, is intimately connected to the challenge of integrating this information into a FAIRinfrastructure [2].Here we present growth optimization of β-Ga2O3 thin films by metalorganic vapor phase epitaxy (MOVPE) on(1 0 0) β-Ga2O3 semi-insulating substrates which is a successful example of applying ML/AI approaches inmaterials synthesis. Improving the efficiency of this synthesis is highly relevant due to the wide range ofelectronic device applications based on Ga2O3. Applying AI modeling on the experimental data related to thinfilm growth, we effectively improved the growth rate and prediction of doping level in MOVPE-grown Si-dopedβ-Ga2O3 [3,4].We will present the implementation of this use case in NOMAD (nomad-lab.eu) [5] and discuss thefunctionalities developed to digitize the entire data lifecycle in crystal growth and epitaxy, with the ultimate goalof FAIR data for materials growth. We developed and deployed Electronic Laboratory Notebooks (ELN) todocument all relevant synthesis procedures. We will show how structured data opens up the potential to createtools that facilitate and automate data management, and the sustainable application of AI-based analytics,dedicated to process optimization in synthesis.[1] Wilkinson, M., et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data.2016; 3, 160018.[2] Scheffler, M., et al. FAIR data enabling new horizons for materials research. Nature. 2022; 604, 635-642.[3] Chou, T.-S. et al. Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga2O3 Thin Films by Deep-Learning Approach. Crystals. 2022; 12(1), 8.[4] Chou, T.-S. et al. Machine learning supported analysis of MOVPE grown β-Ga2O3 thin films on sapphire.Journal of Crystal Growth. 2022; 126737.This work is funded by the NFDI consortium FAIRmat - Deutsche Forschungsgemeinschaft (DFG) - Project 460197019