Machine Learning Exploration of Atomic Heterostructures Towards Perfect Light Absorber and Giant Piezoelectricity

Project Personnel

Tony Low

Principal Investigator

University of Minnesota, Twin Cities

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Steven Koester

University of Minnesota, Twin Cities

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Christopher Hinkle

University of Notre Dame

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Vladimir Cherkassky

University of Minnesota, Twin Cities

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Funding Divisions

Division of Materials Research (DMR)

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.

Publications

Charge-to-spin conversion in twisted graphene/WSe2 heterostructures
S. Lee, D. J. P. de Sousa, Y. Kwon, F. de Juan, Z. Chi, F. Casanova, and T. Low
10/20/2022
How to report and benchmark emerging field-effect transistors
Z. Cheng, C. Pang, P. Wang, S. T. Le, Y. Wu, D. Shahrjerdi, I. Radu, M. C. Lemme, L. Peng, X. Duan, Z. Chen, J. Appenzeller, S. J. Koester, E. Pop, A. D. Franklin, and C. A. Richter
7/29/2022
Convert Widespread Paraelectric Perovskite to Ferroelectrics
H. Wang, F. Tang, M. Stengel, H. Xiang, Q. An, T. Low, and X. Wu
5/12/2022
Methodological framework for materials discovery using machine learning
E. H. Lee, W. Jiang, H. Alsalman, T. Low, and V. Cherkassky
4/14/2022
Data-driven shape memory alloy discovery using Artificial Intelligence Materials Selection (AIMS) framework
W. Trehern, R. Ortiz-Ayala, K. C. Atli, R. Arroyave, and I. Karaman
4/1/2022
Bayesian optimization with adaptive surrogate models for automated experimental design
B. Lei, T. Q. Kirk, A. Bhattacharya, D. Pati, X. Qian, R. Arroyave, and B. K. Mallick
12/3/2021
Simple linear response model for predicting energy band alignment of two-dimensional vertical heterostructures
J. G. Azadani, S. Lee, H. Kim, H. Alsalman, Y. Kwon, J. Tersoff, and T. Low
5/17/2021
Electron-phonon scattering limited intrinsic electrical conductivity of metallic MXenes X2C (X= Ti or Mo)
Z. Jing, J. Liu, N. Li, H. Wang, K. Wu, Y. Cheng, and B. Xiao
10/22/2020
Bandgap engineering of two-dimensional semiconductor materials
A. Chaves, J. G. Azadani, H. Alsalman, D. R. da Costa, R. Frisenda, A. J. Chaves, S. H. Song, Y. D. Kim, D. He, J. Zhou, A. Castellanos-Gomez, F. M. Peeters, Z. Liu, C. L. Hinkle, S. Oh, P. D. Ye, S. J. Koester, Y. H. Lee, P. Avouris, X. Wang, and T. Low
8/24/2020
Materials and Device Strategies for Nanoelectronic 3D Heterogeneous Integration in International Conference on Simulation of Semiconductor Processes and Devices

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Designing Materials to Revolutionize and Engineer our Future (DMREF)