2019
DOI: 10.1007/978-3-030-22741-8_9
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Machine Learning for Performance Enhancement of Molecular Dynamics Simulations

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Cited by 17 publications
(25 citation statements)
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“…Parallel Computing: We know that heterogeneity can lead to difficulty in parallel computing. This is extreme for MLaroundHPC as the ML learnt result can be huge factors (10 5 in our initial example [26]) faster than simulated answers. Further learning can be dynamic within a job and within different runs of a given job.…”
Section: A Hpc For Machine Learningmentioning
confidence: 93%
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“…Parallel Computing: We know that heterogeneity can lead to difficulty in parallel computing. This is extreme for MLaroundHPC as the ML learnt result can be huge factors (10 5 in our initial example [26]) faster than simulated answers. Further learning can be dynamic within a job and within different runs of a given job.…”
Section: A Hpc For Machine Learningmentioning
confidence: 93%
“…It is thus desirable that a "smart" simulation framework provide rapid estimates of these critical output features with high accuracy. MLaroundHPC can enable precisely this as we recently showed that an artificial neural network successfully learns from completed simulation results the desired features associated with the output ionic density profiles to rapidly generate predictions for contact, peak, and center densities in excellent agreement with the results from explicit simulations [26].…”
Section: )mentioning
confidence: 98%
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