The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.
Exploring Thermal Transport in Electrochemical Energy Storage Systems Utilizing Two-Dimensional Materials: Prospects and Hurdles
Two-dimensional materials (e.g., graphene and transition metal dichalcogenides) and their
heterostructures have enormous applications in electrochemical energy storage systems
such as batteries. A comprehensive and solid understanding of these materials’ thermal
transport and mechanism is essential for practical device design. Several advanced experimental
techniques have been developed to measure the intrinsic thermal conductivity
of materials. However, experiments have challenges in providing improved control and
characterization of complex structures, especially for low-dimensional materials. Theoretical
and simulation tools, such as first-principles calculations, Boltzmann transport equations,
molecular dynamics simulations, lattice dynamics simulation, and nonequilibrium Green’s
function, provide reliable predictions of thermal conductivity and physical insights to
understand the underlying thermal transport mechanism in materials. However, doing these
calculations requires high computational resources. The development of new materials
synthesis technology and fast-growing demand for rapid and accurate prediction of physical
properties requires novel computational approaches. The machine learning method provides
a promising solution to address such needs. This review details the recent development in
atomistic/molecular studies and machine learning of thermal transport in two-dimensional
materials. The paper also addresses the latest significant experimental advances. However,
designing the best two-dimensional materials-based heterostructures is like a multivariate
optimization problem. For example, a particular heterostructure may be suitable for thermal
transport but can have lower mechanical strength/stability. For bilayer and multilayer
structures, the interlayer distance may influence the thermal transport properties and
interlayer strength. Therefore, the last part of this review addresses the future research
direction in two-dimensional materials-based heterostructure design for thermal transport in
energy storage systems.
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- Award ID(s):
- 2237990
- NSF-PAR ID:
- 10472637
- Editor(s):
- Chu, Wilson
- Publisher / Repository:
- Annual Review of Heat Transfer
- Date Published:
- Journal Name:
- Annual Review of Heat Transfer
- ISSN:
- 1049-0787
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1615/AnnualRevHeatTransfer.2023049365
