Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine-Learning Interatomic Potentials Enable First-Principles
  Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene
  Heterostructures   One of the ultimate goals of computational modeling in condensed matter is to
be able to accurately compute materials properties with minimal empirical
information. First-principles approaches such as the density functional theory
(DFT) provide the best possible accuracy on electronic properties but they are
limited to systems up to a few hundreds, or at most thousands of atoms. On the
other hand, classical molecular dynamics (CMD) simulations and finite element
method (FEM) are extensively employed to study larger and more realistic
systems, but conversely depend on empirical information. Here, we show that
machine-learning interatomic potentials (MLIPs) trained over short ab-initio
molecular dynamics trajectories enable first-principles multiscale modeling, in
which DFT simulations can be hierarchically bridged to efficiently simulate
macroscopic structures. As a case study, we analyze the lattice thermal
conductivity of coplanar graphene/borophene heterostructures, recently
synthesized experimentally (Sci. Adv. 2019; 5: eaax6444), for which no viable
classical modeling alternative is presently available. Our MLIP-based approach
can efficiently predict the lattice thermal conductivity of graphene and
borophene pristine phases, the thermal conductance of complex
graphene/borophene interfaces and subsequently enable the study of effective
thermal transport along the heterostructures at continuum level. This work
highlights that MLIPs can be effectively and conveniently employed to enable
first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM
simulations, thus expanding the capability for computational design of novel
nanostructures.

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The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
24
The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
1
The categories of this paper are: cs
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: plasm-ph