Work

Determining the structure of functionalized graphene for tailored thermomechanical properties using ML techniques

Year: 2025

Type: article

Abstract: > 0.9 for thermal conductivity, Young's modulus and maximum stress) on a hold-out test set, with mean absolute percentage error (MAPE) remaining below 1% in most cases. Finally, an evolutionary optimization process, in tandem with the trained Machine Learning (ML) models, was employed for finding graphene layouts that possess a set of user-defined target properties. MD-validations of the obtained designs confirmed the applicability of the approach while revealing acceptable deviations for thermal conductivity values and even better alignment for the mechanical properties. In summary, the proposed approach succeeds in a 7 orders of magnitude speedup in estimating the thermomechanical properties of functionalized graphene sheets when compared to pure MD simulations, and up to 6 orders of magnitude faster identification of layouts with prescribed properties, benchmarked on a nanosheet (220 × 100 Å) with 8528 atoms using a 64 core AMD EPYC workstation.

Source: RSC Advances

DOI: 10.1039/d5ra07646c

Authors: Ravil Ashirmametov, Alexandr Alpatov, Farrokh Yousefi, Narges Vafa, Siamac Fazli, Konstantinos Kostas

Institution: Nazarbayev University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,042

Funder: Nazarbayev University