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Automated Parameterization of Quantum Mechanically Derived Force Fields for Soft Materials and Complex Fluids:

J G Vilhena¹, Leandro Greff da Silveira², Paolo Roberto Livotto²

¹Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland.

Journal of Chemical Theory and Computation

|June 29, 2021

Summary

We developed an automated protocol using quantum mechanical data to create accurate molecular dynamics force fields for complex materials. This quantum mechanically derived force field accurately predicts macroscopic properties of liquid crystals.

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Area of Science:

Computational chemistry
Materials science
Soft matter physics

Background:

Molecular dynamics (MD) simulations are crucial for predicting macroscopic properties of complex fluids and soft materials.
The accuracy of MD simulations heavily depends on the quality of the force field (FF).
Existing force fields may not accurately capture the behavior of diverse materials like liquid crystals.

Purpose of the Study:

To present an automated protocol for deriving specific and accurate force fields (FFs) from ab initio quantum mechanical (QM) data.
To extend existing protocols (Joyce and Picky) to larger molecules and complex fluid phases.
To validate the performance of the quantum mechanically derived force field (QMD-FF) against experimental data and other FFs.

Main Methods:

Integration of Joyce and Picky procedures for FF parameterization.
Development of a new automated procedure for intermolecular FF parameterization using QM data.
Incorporation of a fragmentation reconstruction method (FRM) for efficient QM calculations of intermolecular interactions.
Atomistic MD simulations of a benchmark liquid crystal (5CB) in isotropic and nematic phases using different FFs.

Main Results:

The developed QMD-FF demonstrated superior performance compared to general-purpose and hybrid FFs.
The QMD-FF accurately reproduced the isotropic and nematic phases of the liquid crystal across the correct temperature range.
The QMD-FF provided accurate predictions of the structure, dynamics, and thermodynamic properties of the liquid crystal.

Conclusions:

The proposed automated protocol effectively generates accurate QMD-FFs for complex fluids and soft materials.
This method offers a reliable and computationally feasible approach for predicting macroscopic properties.
The validated QMD-FF protocol can be applied to other complex fluids and soft materials.