DMREF: Collaborative Research: Machine learning exploration of atomic heterostructures towards perfect light absorber and giant piezoelectricity

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AbstractNontechnical Description: Recent discovery of atomically thin two-dimensional (2D) materials, such as graphene, transition metal dichalcogenides, black phosphorus, among many others, has opened new opportunities to atomic level materials control through vertical stacking, i.e. 2D heterostructure. The art of 2D heterostructure materials design is at its nascent stage, and with the potential library of 2D monolayers that we can access experimentally amounting to about 1000, just the mere lowest energy stacking of N monolayers would then lead to 1000N possible heterostructures. Our program seeks a transformational impact on the discovery process of useful 2D heterostructures, such as perfect light absorption in the visible spectrum and giant piezoelectricity, through novel machine-learning (ML)-guided density functional theory (DFT). We have assembled a team comprising of experts in data science and the application of ML, modeling of 2D materials and their optical properties, molecular beam epitaxial growth of 2D heterostructures, and materials and device characterization. The successful demonstration of these new designer 2D heterostructures would usher in a new era of efficient and purposeful materials design methodology. A three-pronged broadening participation program for all participating groups in this proposal is planned, which includes undergraduate research, community outreach, and summer programs.Technical Description: Research in two-dimensional atomic crystals has recently focused on their heterostructures, and the advancements in this emerging field has already led to fascinating discoveries such as superconductivity and magnetism. However, thousands of different 2D layered materials and their permutations amount to almost infinite heterostructure combinations. This research will develop a novel ML-guided DFT framework, in conjunction with physically motivated atomistic descriptors, which applies data science in the search for designer heterostructures with targeted properties. As a proof-of-concept, we will demonstrate heterostructures with perfect light absorption through optimizing the band nesting between the filled and empty bands as well as giant piezoelectricity through engineering the electronegativity dipole moments. These heterostructures identified with the targeted properties will be grown with ultra-clean state-of-the-art MBE approaches, and their absorption and piezoelectric coefficients characterized. Corroboration between experiments and theory will then instruct on possible improvements to the proposed ML and DFT models and overall strategy. The successful demonstration of these new designer 2D heterostructures would usher in a new era of efficient and purposeful materials design methodology.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.