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- W4383301902 endingPage "385702" @default.
- W4383301902 startingPage "385702" @default.
- W4383301902 abstract "Abstract Diamond-like carbon (DLC) films have broad application potential due to their high hardness, high wear resistance, and self-lubricating properties. However, considering that DLC films are micron-scale, neither finite element methods nor macroscopic experiments can reveal their deformation and failure mechanisms. Here we propose a coarse-grained molecular dynamics (CGMD) approach which expands the capabilities of molecular dynamics simulations to uniaxial tensile behavior of DLC films at a higher scale. The Tersoff potential is modified by high-throughput screening calculations for CGMD. Given this circumstance, machine learning (ML) models are employed to reduce the high-throughput computational cost by 86%, greatly improving the efficiency of parameter optimization in second- and fourth-order CGMD. The final obtained coarse-grained tensile curves fit well with that of the all-atom curves, showing that the ML-based CGMD method can investigate DLC films at higher scales while saving a large number of computational resources, which is important for promoting the research and production of high-performance DLC films." @default.
- W4383301902 created "2023-07-07" @default.
- W4383301902 creator A5066915711 @default.
- W4383301902 creator A5078632164 @default.
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- W4383301902 creator A5087775867 @default.
- W4383301902 date "2023-07-06" @default.
- W4383301902 modified "2023-10-18" @default.
- W4383301902 title "A coarse-grained study on mechanical behaviors of diamond-like carbon based on machine learning" @default.
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- W4383301902 doi "https://doi.org/10.1088/1361-6528/acde5a" @default.
- W4383301902 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37410425" @default.
- W4383301902 hasPublicationYear "2023" @default.
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