Data-Driven Machine and Deep Learning-Based Prediction of Interfacial Thermal Resistance Using High-Fidelity Molecular Dynamics Simulations

The van der Waals heterostructure (vdWH) of Stanene/Hexagonal Boron Nitride (Sn/h-BN) nanosheet has drawn attention due to its promising electro-thermal properties. Understanding the interfacial thermal resistance (ITR) between layers is essential for

Authors

Sakib Javed (ORCID: https://orcid.org/0000-0002-8057-2353)
Priom Das (ORCID: https://orcid.org/0009-0004-0109-6654)
Sadia Tasnim (ORCID: https://orcid.org/0000-0001-6720-5578)
Plabon Paul
Monjur Morshed AKM

Institutions

Bangladesh University of Engineering and Technology (BD)

Publication Details

Published: 2025-09-01
DOI: https://doi.org/10.22541/au.175673730.05478892/v1
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 0.00

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article

Data-Driven Machine and Deep Learning-Based Prediction of Interfacial Thermal Resistance Using High-Fidelity Molecular Dynamics Simulations

Sakib Javed, Priom Das, Sadia Tasnim, Plabon Paul et al.

Sep 1, 2025

Machine Learning in Materials Science

article

Data-Driven Machine and Deep Learning-Based Prediction of Interfacial Thermal Resistance Using High-Fidelity Molecular Dynamics Simulations

Sakib Javed, Priom Das, Sadia Tasnim, Plabon Paul, Monjur Morshed AKM

September 1, 2025article en

Abstract

The van der Waals heterostructure (vdWH) of Stanene/Hexagonal Boron Nitride (Sn/h-BN) nanosheet has drawn attention due to its promising electro-thermal properties. Understanding the interfacial thermal resistance (ITR) between layers is essential for

Bangladesh University of Engineering and Technology (BD)

Openalex Percentile: Top 83%

Machine Learning in Materials Science

DOI Open Access PDF OpenAlex

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