Efficient high-throughput method utilizing neural network potentials to calculate interaction energies, validated by clean transfer experiment of CVD graphene with polymer mixtures

Averitt, Jared Keith; Pourianejad, Sajedeh; Ayodele, Olubunmi O.; Schmidt, Kirby; Trofe, Anthony; Starobin, Joseph; Ignatova, Tetyana

doi:10.1016/j.carbon.2024.119336

Carbon

2024

DOI: 10.1016/j.carbon.2024.119336

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Efficient high-throughput method utilizing neural network potentials to calculate interaction energies, validated by clean transfer experiment of CVD graphene with polymer mixtures

Jared Keith Averitt,

Sajedeh Pourianejad,

Olubunmi O. Ayodele

et al.

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Assessment of Classical Force-Fields for Graphene Mechanics

Ma,

Tan,

Cai

et al. 2024

Crystals

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The unique properties of graphene have attracted the interest of researchers from various fields, and the discovery of graphene has sparked a revolution in materials science, specifically in the field of two-dimensional materials. However, graphene synthesis’s costly and complex process significantly impairs researchers’ endeavors to explore its properties and structure experimentally. Molecular dynamics simulation is a well-established and useful tool for investigating graphene’s atomic structure and dynamic behavior at the nanoscale without requiring expensive and complex experiments. The accuracy of the molecular dynamics simulation depends on the potential functions. This work assesses the performance of various potential functions available for graphene in mechanical properties prediction. The following two cases are considered: pristine graphene and pre-cracked graphene. The most popular fifteen potentials have been assessed. Our results suggest that diverse potentials are suitable for various applications. REBO and Tersoff potentials are the best for simulating monolayer pristine graphene, and the MEAM and the AIREBO-m potentials are recommended for those with crack defects because of their respective utilization of the electron density and inclusion of the long-range interaction. We recommend the AIREBO-m potential for a general case of classical molecular dynamics study. This work might help to guide the selection of potentials for graphene simulations and the development of further advanced interatomic potentials.

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Assessment of Classical Force-Fields for Graphene Mechanics

Ma,

Tan,

Cai

et al. 2024

Crystals

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Efficient high-throughput method utilizing neural network potentials to calculate interaction energies, validated by clean transfer experiment of CVD graphene with polymer mixtures

Cited by 1 publication

References 62 publications

Assessment of Classical Force-Fields for Graphene Mechanics

Assessment of Classical Force-Fields for Graphene Mechanics

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