Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential
Penghua Ying, Ting Liang, Ke Xu, Jin Zhang, Jianbin Xu, Jianyang Wu,, Zheyong Fan, Tapio Ala-Nissila, Zheng Zhong

TL;DR
This study develops a machine learning potential for different phosphorene allotropes and uses molecular dynamics simulations to analyze their thermal transport properties, revealing significant differences and strain effects.
Contribution
The paper introduces a novel neuroevolution-based machine learning potential for black, blue, and violet phosphorene, enabling accurate and efficient thermal transport simulations.
Findings
Black-P has anisotropic thermal conductivity with 12.5 and 78.4 W/mK in different directions.
Blue-P exhibits high thermal conductivity of 128 W/mK, while Violet-P has very low conductivity of 2.36 W/mK.
Strain affects thermal conductivity differently, with blue-P showing unbounded behavior under tension.
Abstract
Phosphorus has diverse chemical bonds and even in its two-dimensional form there are three stable allotropes: black phosphorene (Black-P), blue phosphorene (Blue-P), and violet phosphorene (Violet-P). Due to the complexity of these structures, no efficient and accurate classical interatomic potential has been developed for them. In this paper, we develop an efficient machine-learned neuroevolution potential model for these allotropes and apply it to study thermal transport in them via extensive molecular dynamics (MD) simulations. Based on the homogeneous nonequilibrium MD method, the thermal conductivities are predicted to be (Black-P in armchair direction), (Black-P in zigzag direction), (Blue-P), and (Violet-P) . The underlying reasons for the significantly different thermal conductivity values in these…
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Taxonomy
TopicsThermal properties of materials · Machine Learning in Materials Science · Fuel Cells and Related Materials
