Open boundary modeling in molecular dynamics with machine learning
Publication date
2020
Document type
Conference paper
Author
Wittmer, Niklas
Organisational unit
Scopus ID
ISBN
Conference
20th International Conference on Computational Science ICCS 2020
Series or journal
Lecture notes in computer science
Periodical volume
12142
First page
334
Last page
347
Part of the university bibliography
✅
Abstract
Molecular-continuum flow simulations combine molecular dynamics (MD) and computational fluid dynamics for multiscale considerations. A specific challenge in these simulations arises due to the “open MD boundaries” at the molecular-continuum interface: particles close to these boundaries do not feel any forces from outside which results in unphysical behavior and incorrect thermodynamic pressures. In this contribution, we apply neural networks to generate approximate boundary forces that reduce these artefacts. We train our neural network with force-distance pair values from periodic MD simulations and use this network to later predict boundary force contributions in non-periodic MD systems. We study different training strategies in terms of MD sampling and training for various thermodynamic state points and report on accuracy of the arising MD system. We further discuss computational efficiency of our approach in comparison to existing boundary force models.
Version
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