(63d) Automation of Non-Bonded Parameter Optimization in Molecular Mechanics Force Fields | AIChE

(63d) Automation of Non-Bonded Parameter Optimization in Molecular Mechanics Force Fields


Barhaghi, M. - Presenter, Wayne State University
Nejahi, Y., Wayne State University
Messerly, R. A., National Renewable Energy Laboratory
Schwiebert, L., Wayne State University
Potoff, J., Wayne State University
In this work, we describe an automated process for the optimization of non-bonded interactions in molecular mechanics force fields to reproduce selected experimental data, such as vapor-liquid coexistence densities. A multi-scale optimization process is proposed. The particle swam optimization (PSO) method[1] is combined with isobaric-isothermal ensemble Monte Carlo simulations at two state points to provide an estimate of the optimal parameters. The PSO method allows for the efficient evaluation of a wide range of parameter values, enhancing the probability of finding a global minimum. Additionally, with the PSO method, it is possible to perform multi-dimensional optimization of parameters, producing insights into relationships between various parameters and physical properties that may be missed using lower dimensional optimization strategies. These estimated parameters as used as input to canonical isothermal-isochoric Monte Carlo[2] or grand canonical histogram reweighting Monte Carlo simulations[3], whose resulting output are analyzed with the Multistate Bennett Acceptance Ratio (MBAR) method[4] to determine the final optimized parameters. Illustrative examples are presented for the optimization of Mie potential parameters for cyclic alkanes.


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