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Molecular dynamics at the atomistic scale is increasingly being used to predict material properties and speed up the materials design and development process. However, the accuracy of molecular dynamics predictions is sensitively dependent on the force fields. In the traditional force field calibration process, a specific property, predicted by the model, is compared with the experimental observation and the force field parameters are adjusted to minimize the difference. This leads to the issue that the calibrated force fields are not generic and robust enough to predict different properties. Here, a new calibration method based on multi-objective Bayesian optimization is developed to speed up the development of molecular dynamics force fields that are capable of predicting multiple properties accurately. This is achieved by reducing the number of simulation runs to generate the Pareto front with an efficient sequential sampling strategy. The methodology is demonstrated by generating a new coarse-grained force field for polycaprolactone, where the force field can predict mechanical properties and water diffusion in the polymer.
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Machine Learning-Enabled Optimization of Force Fields for Hydrofluorocarbons
In this work surrogate assisted optimization is utilized to calibrate predictive molecular models, called force fields, used in molecular simulations to reproduce the liquid density of a hydrofluorocarbon refrigerant molecule. A previous calibration workflow which relied on Gaussian process regression models and large Latin hypercube samples to screen force field parameter space is extended to include Bayesian optimization methods to efficiently guide the search for force field parameters. In comparison to the previous work, the Bayesian-based calibration workflow finds a parameter set which results in a lower objective function value than the original workflow after evaluating approximately 50% fewer parameter sets. It is envisioned that this updated workflow will facilitate rapid force field optimization enabling screening of vast molecular design space.
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- Award ID(s):
- 1917474
- PAR ID:
- 10403696
- Editor(s):
- Yamashita, Y.; Kano, M.
- Date Published:
- Journal Name:
- Computing and computers
- Volume:
- 49
- ISSN:
- 0794-0920
- Page Range / eLocation ID:
- 1249-1254
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/B978-0-323-85159-6.50208-6
