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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2604.25941 |
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| _version_ | 1866908999382728704 |
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| author | Hatami, Faranak de Almeida, Valmor F. |
| author_facet | Hatami, Faranak de Almeida, Valmor F. |
| contents | An iterative optimization algorithm with MD simulations in the loop is developed and
applied to optimize Lennard-Jones (LJ) parameters specific for liquid
tri-n-butyl phosphate (TBP). The optimization loop uses non-dominated sorting
genetic algorithms to obtain LJ parameters that reproduce key properties such
as mass density, electric dipole moment, heat of vaporization, self-diffusion
coefficient (SDC), and shear viscosity. Errors relative to
experimentally measured properties lead to a multi-objective function optimization
problem stated in terms of a Pareto-optimal set. A systematic application of the
optimization algorithm to cases involving single- and multi-objective functions was
carried out in this work, establishing a framework for atomistic TBP property
predictions. We demonstrate the use of a neural network property model to amortize the
high cost of MD simulations in the optimization loop and to allow for large populations
and more generations to be used in the genetic algorithms. In our previous study of
finding the best force field for TBP property predictions as judged by the
aforementioned thermophysical properties, we found the Polarized AMBER-MNDO force field
to be the best overall showing a \num{74}\% relative deviation from experimental values.
However, in this study, we show optimized values of the LJ parameters that improve the
overall deviation from experimental data to \num{23}\% when using the NN NSGA-III
algorithm. Despite this large improvement, the accurate prediction of the transport
properties, SDC and shear viscosity, remains difficult since improvements in one of them
worsen the other, and vice versa. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25941 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Molecular Dynamics Force Field Genetic Optimization for Tri-n-butyl Phosphate Liquid Hatami, Faranak de Almeida, Valmor F. Chemical Physics Computational Physics An iterative optimization algorithm with MD simulations in the loop is developed and applied to optimize Lennard-Jones (LJ) parameters specific for liquid tri-n-butyl phosphate (TBP). The optimization loop uses non-dominated sorting genetic algorithms to obtain LJ parameters that reproduce key properties such as mass density, electric dipole moment, heat of vaporization, self-diffusion coefficient (SDC), and shear viscosity. Errors relative to experimentally measured properties lead to a multi-objective function optimization problem stated in terms of a Pareto-optimal set. A systematic application of the optimization algorithm to cases involving single- and multi-objective functions was carried out in this work, establishing a framework for atomistic TBP property predictions. We demonstrate the use of a neural network property model to amortize the high cost of MD simulations in the optimization loop and to allow for large populations and more generations to be used in the genetic algorithms. In our previous study of finding the best force field for TBP property predictions as judged by the aforementioned thermophysical properties, we found the Polarized AMBER-MNDO force field to be the best overall showing a \num{74}\% relative deviation from experimental values. However, in this study, we show optimized values of the LJ parameters that improve the overall deviation from experimental data to \num{23}\% when using the NN NSGA-III algorithm. Despite this large improvement, the accurate prediction of the transport properties, SDC and shear viscosity, remains difficult since improvements in one of them worsen the other, and vice versa. |
| title | Molecular Dynamics Force Field Genetic Optimization for Tri-n-butyl Phosphate Liquid |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2604.25941 |