A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers

A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers. Caccin, M., Li, Z., Kermode, J. R., & Vita, A. D. International Journal of Quantum Chemistry, 115(16):1129–1139, John Wiley & Sons, August, 2016.

Paper abstract bibtex

Recent advances in quantum mechanical (QM)-based molecular dynamics (MD) simulations have used machine-learning (ML) to predict, rather than recalculate, QM-accurate forces in atomic configurations sufficiently similar to previously encountered ones. Here, we discuss how ML approaches can be deployed within large-scale QM/MM materials simulations on massively parallel supercomputers, making QM zones of $\gtrsim$1000 atoms routinely attainable. We argue that the ML approach allows computational effort to be concentrated on the most chemically active subregions of the QM zone, significantly improving the overall efficiency of the simulation. We thus propose a novel method to partition large QM regions into multiple subregions, which can be computed in parallel to achieve optimal scaling. Then we review a recently proposed QM/ML MD scheme (Z. Li, J.R. Kermode, A. De Vita Phys. Rev. Lett., 2015, 114, 096405), discussing how this could be efficiently combined with QM-zone partitioning.

@article{wrap68012,
          volume = {115},
          number = {16},
           month = {August},
          author = {Marco Caccin and Zhenwei Li and James R. Kermode and Alessandro De Vita},
           title = {A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers},
       publisher = {John Wiley \& Sons},
            year = {2016},
         journal = {International Journal of Quantum Chemistry},
           pages = {1129--1139},
        keywords = {machine learning; quantum mechanics/molecular mechanics; HPC; fracture; partitioning},
             url = {https://wrap.warwick.ac.uk/68012/},
        abstract = {Recent advances in quantum mechanical (QM)-based molecular dynamics (MD) simulations have used machine-learning (ML) to predict, rather than recalculate, QM-accurate forces in atomic configurations sufficiently similar to previously encountered ones. Here, we discuss how ML approaches can be deployed within large-scale QM/MM materials simulations on massively parallel supercomputers, making QM zones of {$\gtrsim$}1000 atoms routinely attainable. We argue that the ML approach allows computational effort to be concentrated on the most chemically active subregions of the QM zone, significantly improving the overall efficiency of the simulation. We thus propose a novel method to partition large QM regions into multiple subregions, which can be computed in parallel to achieve optimal scaling. Then we review a recently proposed QM/ML MD scheme (Z. Li, J.R. Kermode, A. De Vita Phys. Rev. Lett., 2015, 114, 096405), discussing how this could be efficiently combined with QM-zone partitioning.}
}

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