Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures

You, Huaiqian Q.; Xu, Xiao; Yu, Yue; Silling, Stewart A.; D’Elia, Marta; Foster, John

doi:10.1007/s10483-023-2996-8

Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures

Journal Article · Mon Jul 03 00:00:00 EDT 2023 · Applied Mathematics and Mechanics

DOI:https://doi.org/10.1007/s10483-023-2996-8· OSTI ID:2426041

You, Huaiqian Q. ^[1]; Xu, Xiao ^[2]; Yu, Yue ^[1]; Silling, Stewart A. ^[3]; D’Elia, Marta ^[4]; Foster, John ^[2]

Lehigh Univ., Bethlehem, PA (United States)
Univ. of Texas, Austin, TX (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: National Science Foundation (NSF); US Air Force Office of Scientific Research (AFOSR); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: NA0003525

OSTI ID:: 2426041

Report Number(s):: SAND--2024-10027J

Journal Information:: Applied Mathematics and Mechanics, Journal Name: Applied Mathematics and Mechanics Journal Issue: 7 Vol. 44; ISSN 0253-4827

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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