Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials

Abstract

Examination of thermal expansion of two-dimensional (2D) nanomaterials is a challenging theoretical task with either ab-initio or classical molecular dynamics simulations. In this regard, while ab-initio molecular dynamics (AIMD) simulations offer extremely accurate predictions, but they are excessively demanding from computational point of view. On the other side, classical molecular dynamics simulations can be conducted with affordable computational costs, but without predictive accuracy needed to study novel materials and compositions. Herein, we explore the thermal expansion of several carbon-based nanosheets on the basis of machine-learning interatomic potentials (MLIPs). We show that passively trained MLIPs over inexpensive AIMD trajectories enable the examination of thermal expansion of complex nanomembranes over wide range of temperatures. Passively fitted MLIPs could also with outstanding accuracy reproduce the phonon dispersion relations predicted by density functional theory calculations. Our results highlight that the devised methodology on the basis of passively trained MLIPs is computationally efficient and versatile to accurately examine the thermal expansion of complex and novel materials and compositions using the molecular dynamics simulations.

References in corpus (11)

• Electric Field Effect in Atomically Thin Carbon Films
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• High-accuracy first-principles determination of the structural, vibrational and thermodynamical properties of diamond, graphite, and derivatives
• Negative Thermal Expansion Coefficient of Graphene Measured by Raman Spectroscopy
• Active learning of linearly parametrized interatomic potentials
• Phagraphene: A Low-energy Graphene Allotrope composed of 5-6-7 Carbon Rings with Distorted Dirac Cones
• Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
• Prediction of C7N6 and C9N4: Stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties
• Thermal conductivity of graphene polymorphs and compounds: from C3N to graphdiyne lattices
• Ultrahigh thermal conductivity and strength in direct-gap semiconducting graphene-like BC6N: A first-principles and classical investigation
• Interplay between Raman shift and thermal expansion in graphene: temperature-dependent measurements and analysis of substrate corrections

Cited by in corpus (8)

• Structural, electronic, thermal and mechanical properties of C60-based fullerene two-dimensional networks explored by first-principles and machine learning
• Quantum Optics Applications of Hexagonal Boron Nitride Defects
• Navigating the Evolution of Two-dimensional Carbon Nitride Research: Integrating Machine Learning into Conventional Approaches
• Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
• Lattice thermal conductivity and elastic modulus of XN4 (X=Be, Mg and Pt) 2D materials using machine learning interatomic potentials
• A Concise Review of Recently Synthesized 2D Carbon Allotropes: Amorphous Carbon, Graphynes, Biphenylene and Fullerene Networks
• Anomalous tensile strength and thermal expansion, and low thermal conductivity in wide band gap boron monoxide monolayer
• Multiscale mechanics of thermal gradient coupled graphene fracture: A molecular dynamics study