TY - GEN

T1 - Machine Learning Based Exploitation and Characterization of 2D Materials

AU - Zhuang, Xiaoying

N1 - Conference code: 18

PY - 2023

Y1 - 2023

N2 - 2D materials have attracted widespread attention in recent years. They have some unique properties that other usual materials do not have. For example, its electrical, mechanical, thermal and optical properties vary with the number of layers. Density functional theory (DFT) calculations are robust tools to explore the physical properties of pristine structures as well as to explore new type of 2D nanomaterials at their ground state, but they become exceedingly expensive for large systems or at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. We developed machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio datasets which can be used to evaluate the complex physical properties of nanostructured materials, with only a fractional computational cost of conventional DFT-based solutions, cutting down from months to tens of hours. MLIPs offer extraordinary capabilities to marry the first-principles accuracy with multiscale modeling and thus enable the modeling of complex nanostructures at continuum level and has flexibility without paying unaffordable computational costs. We show outstanding and robust potential to develop fully automated platforms, to design, optimize and explore various properties, i.e. mechanical, thermal, optical and electrical properties of 2D materials and structures at continuum level, and with inherent precision and robustness.

AB - 2D materials have attracted widespread attention in recent years. They have some unique properties that other usual materials do not have. For example, its electrical, mechanical, thermal and optical properties vary with the number of layers. Density functional theory (DFT) calculations are robust tools to explore the physical properties of pristine structures as well as to explore new type of 2D nanomaterials at their ground state, but they become exceedingly expensive for large systems or at finite temperatures. Classical molecular dynamics (CMD) simulations offer the possibility to study larger systems at elevated temperatures, but they require accurate interatomic potentials. We developed machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio datasets which can be used to evaluate the complex physical properties of nanostructured materials, with only a fractional computational cost of conventional DFT-based solutions, cutting down from months to tens of hours. MLIPs offer extraordinary capabilities to marry the first-principles accuracy with multiscale modeling and thus enable the modeling of complex nanostructures at continuum level and has flexibility without paying unaffordable computational costs. We show outstanding and robust potential to develop fully automated platforms, to design, optimize and explore various properties, i.e. mechanical, thermal, optical and electrical properties of 2D materials and structures at continuum level, and with inherent precision and robustness.

UR - http://www.scopus.com/inward/record.url?scp=85182026810&partnerID=8YFLogxK

U2 - 10.1109/NMDC57951.2023.10343692

DO - 10.1109/NMDC57951.2023.10343692

M3 - Conference contribution

AN - SCOPUS:85182026810

SN - 979-8-3503-3547-7

BT - 2023 IEEE Nanotechnology Materials and Devices Conference, NMDC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE Nanotechnology Materials and Devices Conference (IEEE-NMDC 2023)

Y2 - 22 October 2023 through 25 October 2023

ER -