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A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua

  • Extreme Learning Machine and Deep Learning Networks
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Abstract

In the community of computational materials science, one of the challenges in hierarchical multiscale modeling is information-passing from one scale to another, especially from the molecular model to the continuum model. A machine-learning-enhanced approach, proposed in this paper, provides an alternative solution. In the developed hierarchical multiscale method, molecular dynamics simulations in the molecular model are conducted first to generate a dataset, which represents physical phenomena at the nanoscale. The dataset is then used to train a material failure/defect classification model and stress regression models. Finally, the well-trained models are implemented in the continuum model to study the mechanical behaviors of materials at the macroscale. Multiscale modeling and simulation of a molecule chain and an aluminum crystalline solid are presented as the applications of the proposed method. In addition to support vector machines, extreme learning machines with single-layer neural networks are employed due to their computational efficiency.

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Authors and Affiliations

  1. Department of Mechanical Engineering, The University of Iowa, Iowa City, USA

    Shaoping Xiao & Siamak Attarian

  2. Department of Industrial and System Engineering, The University of Iowa, Iowa City, USA

    Renjie Hu & Zhen Li

  3. Arcada University of Applied Sciences, Helsinki, Finland

    Kaj-Mikael Björk & Amaury Lendasse

  4. Hanken School of Economics, Helsinki, Finland

    Kaj-Mikael Björk

  5. Information and Logistics Technology Department, The University of Houston, Houston, USA

    Amaury Lendasse

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  1. Shaoping Xiao
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  2. Renjie Hu
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  3. Zhen Li
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  4. Siamak Attarian
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  5. Kaj-Mikael Björk
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  6. Amaury Lendasse
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Correspondence to Renjie Hu.

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Xiao, S., Hu, R., Li, Z. et al. A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua. Neural Comput & Applic 32, 14359–14373 (2020). https://doi.org/10.1007/s00521-019-04480-7

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