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  • Coarse-Grained Force Field Calibration Based on Multi-Objective Bayesian Optimization to Simulate Water Diffusion in Poly-ɛ-caprolactone
Title: Coarse-Grained Force Field Calibration Based on Multi-Objective Bayesian Optimization to Simulate Water Diffusion in Poly-ɛ-caprolactone
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.  more » « less
Award ID(s):
1663227
NSF-PAR ID:
10158364
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
The Journal of Physical Chemistry A
ISSN:
1089-5639
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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