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ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data.

Federico Zahariev¹, Nuwan De Silva¹, Mark S Gordon¹

¹Department of Chemistry and Ames Laboratory, Iowa State University , Ames, Iowa 50011, United States.

Journal of Chemical Information and Modeling

|February 8, 2017

Summary

This summary is machine-generated.

ParFit is a new open-source program that automates fitting molecular mechanics parameters to quantum mechanical data. This tool enhances parameter transferability across multiple molecules using genetic algorithms.

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Area of Science:

Computational Chemistry
Quantum Chemistry
Molecular Mechanics

Background:

Accurate molecular mechanics (MM) force fields are crucial for simulating large molecular systems.
Parameterization, the process of fitting MM force fields to high-level quantum mechanical (QM) or ab initio data, is computationally intensive and challenging.
Existing methods often struggle with simultaneous optimization of multiple parameters across different molecules, limiting parameter transferability.

Purpose of the Study:

To introduce ParFit, a novel object-oriented program designed to automate the fitting of molecular mechanics parameters to ab initio data.
To develop a robust and efficient parameterization tool that enhances the transferability of fitted parameters.
To provide an open-source, accessible solution for the computational chemistry community.

Main Methods:

ParFit employs a hybrid approach combining deterministic and stochastic genetic algorithms for parameter optimization.
The program is capable of simultaneously optimizing multiple molecular mechanics parameters for several molecules.
It supports the application of symmetric and antisymmetric constraints during the optimization process.

Main Results:

ParFit successfully automates the fitting of molecular mechanics parameters to ab initio data.
The simultaneous handling of multiple molecules and constrained optimization significantly enhances parameter transferability.
The program was successfully applied to parameterize a series of phosphine oxides relevant to metal extraction chemistry.

Conclusions:

ParFit offers an efficient and versatile solution for automating molecular mechanics parameterization.
The enhanced parameter transferability achieved by ParFit is critical for accurate simulations of diverse chemical systems.
As an open-source program, ParFit is readily available to advance research in computational chemistry and related fields.