CAREER:Hybrid Data-driven Synthesis by Design of Atomically Thin Quantum Materials

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Two-dimensional (2D) crystalline materials consisting of a single atomic layer have unique quantum mechanical properties that are critical for several advanced technological applications such as photovoltaic and electronic devices. However, the synthesis of 2D materials is generally accomplished through exhaustive trial-and-error experimentation that hinders their commercial exploitation. The main impeding factors are the lack of a comprehensive understanding of the underlying growth mechanisms and the lack of real-time measurement of growth states for implementing feedback process control. The research goals of this CAREER project are to (i) develop a computational model to understand the mechanisms governing the growth of 2D materials, (ii) build a database relating the synthesis process to the properties of these materials, and (iii) use artificial intelligence to find the optimum synthesis conditions. The proposed integration of research and education includes course development and laboratory modules for undergraduate and graduate students and research internships for undergraduate students. The outreach program will engage K-12 students and teachers as well as faculty and minority students from a local minority-serving institution.The proposed research focuses on developing a unified design multiscale framework addressing the growth of 2D materials using the more complex chemical vapor deposition-variant techniques that involve reactive flows of precursors. The objective is to understand the growth mechanisms, such as growth chemistry, and effect of different growth parameters, such as carrier gas flow rates, on the morphology and characteristics of the synthesized 2D materials. This multiscale framework will also be used to build a database of synthesis-morphology conditions to guide the design of new 2D materials. The developed synthesis-morphology database will be used in combination with the ML models, specifically Generative adversarial networks, to predict the morphology and properties of 2D materials significantly faster than the multiscale model. The objective is to develop a model that can be used as an observer with small enough response time that can be useful for real-time control of the synthesis process. The project will also focus on addressing the inverse problem of finding the optimal conditions for growing 2D quantum materials with desired properties. This problem will be first transformed into a classification problem using the synthesis-morphology database, which will be solved utilizing the ML models, and specifically Convolutional Neural Networks. Collaboration with industrial partners is planned through the I/UCRC Center for Atomically Thin Multifunctional Coatings (ATOMIC). The research results will be integrated into a new technical elective course and an existing undergraduate course on engineering materials. A light web-based version of the simulation software will be used for outreach activities and will be made available publicly through the website of the NSF-funded 2D Crystal Consortium - Materials Innovation Platform (2DCC). The outreach program will focus on engaging (1) K-12 students and teachers through STEM training camps and (2) faculty and students belonging to underrepresented minority groups in STEM from a local HBCU, Grambling State University, through computational teaching modules related to the Materials Genome initiative.This project is jointly funded by the Process Systems, Reaction Engineering, and Molecular Thermodynamics Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.