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Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

Engineering and Technology

Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

S. Tian, K. Zhou, et al.

Discover the groundbreaking research by Shun Tian, Ke Zhou, Wanjian Yin, and Yilun Liu, which reveals how in-plane tensile stiffness and out-of-plane bending stiffness can classify thermal expansion in 2D crystals. This study paves the way for the design of 2D Invar monolayers with zero thermal expansion and anti-Invar monolayers with extreme thermal behavior, advancing the field of nanoscale electronics.

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Playback language: English
Abstract
This work identifies in-plane tensile stiffness and out-of-plane bending stiffness as effective descriptors for classifying positive and negative thermal expansion in 2D crystals. Using high-throughput calculations and symbolic regression, these descriptors facilitated the discovery of 2D Invar monolayers (zero thermal expansion) and 2D anti-Invar monolayers (extremely high positive or negative thermal expansion). This research is significant for advancing next-generation nanoscale electronics.
Publisher
Nature Communications
Published On
Aug 14, 2024
Authors
Shun Tian, Ke Zhou, Wanjian Yin, Yilun Liu
Tags
thermal expansion
2D crystals
Invar monolayers
anti-Invar monolayers
nanoscale electronics
high-throughput calculations
symbolic regression

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