easyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms

The dynamics of metal centers are challenging to describe due to the vast variety of ligands, metals, and coordination spheres, hampering the existence of general databases of transferable force field parameters for classical molecular dynamics simulations. Here, we present easyPARM, a Python-based tool that can calculate force field parameters for a wide range of metal complexes from routine frequency calculations with electronic structure methods. The approach is based on a unique labeling strategy, in which each ligand atom that coordinates the metal receives a unique atom type. This design prevents parameter shortage, labeling duplication, and the necessity to post-process output files, even for very complicated coordination spheres, whose parametrization process remain automatic. The program requires the Cartesian Hessian matrix, the geometry <i>xyz</i> file, and the atomic charges to provide reliable force-field parameters extensively benchmarked against density functional theory dynamics in both the gas and condensed phases. The procedure allows the classical description of metal complexes at a low computational cost with an accuracy as good as the quality of the Hessian matrix obtained by quantum chemistry methods. easyPARM v2.00 reads vibrational frequencies and charges in Gaussian (version 09 or 16) or ORCA (version 5 or 6) format and provides refined force-field parameters in Amber format. These can be directly used in Amber and NAMD molecular dynamics engines or converted to other formats. The tool is available free of charge in the GitHub platform (https://github.com/Abdelazim-Abdelgawwad/easyPARM.git).

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-02-06
DOI
https://doi.org/10.1021/acs.jctc.4c01272
Citations
8
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
4.14

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article

easyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms

Antonio Francés‐Monerris, Abdelazim M. A. Abdelgawwad
8 citations
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
4.14
article

easyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms

Antonio Francés‐Monerris, Abdelazim M. A. Abdelgawwad
article en
8 citations

Abstract

The dynamics of metal centers are challenging to describe due to the vast variety of ligands, metals, and coordination spheres, hampering the existence of general databases of transferable force field parameters for classical molecular dynamics simulations. Here, we present easyPARM, a Python-based tool that can calculate force field parameters for a wide range of metal complexes from routine frequency calculations with electronic structure methods. The approach is based on a unique labeling strategy, in which each ligand atom that coordinates the metal receives a unique atom type. This design prevents parameter shortage, labeling duplication, and the necessity to post-process output files, even for very complicated coordination spheres, whose parametrization process remain automatic. The program requires the Cartesian Hessian matrix, the geometry <i>xyz</i> file, and the atomic charges to provide reliable force-field parameters extensively benchmarked against density functional theory dynamics in both the gas and condensed phases. The procedure allows the classical description of metal complexes at a low computational cost with an accuracy as good as the quality of the Hessian matrix obtained by quantum chemistry methods. easyPARM v2.00 reads vibrational frequencies and charges in Gaussian (version 09 or 16) or ORCA (version 5 or 6) format and provides refined force-field parameters in Amber format. These can be directly used in Amber and NAMD molecular dynamics engines or converted to other formats. The tool is available free of charge in the GitHub platform (https://github.com/Abdelazim-Abdelgawwad/easyPARM.git).

Journal of Chemical Theory and ComputationVol. 21(4)
Universitat de València (ES)
Ministerio de Ciencia, Innovación y Universidades, Generalitat Valenciana, European Regional Development Fund, Agencia Estatal de Investigación, Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana, European Social Fund
Openalex Percentile: Top 6%
Machine Learning in Materials Science
4.14
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