Driving torsion scans with wavefront propagation

The parameterization of torsional/dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields. Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field...

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Bibliographic Details
Published in:The Journal of chemical physics 2020-06, Vol.152 (24), p.244116-244116
Main Authors: Qiu, Yudong, Smith, Daniel G. A., Stern, Chaya D., Feng, Mudong, Jang, Hyesu, Wang, Lee-Ping
Format: Article
Language:English
Subjects:
Algorithms
Computer networks
Constraints
Dihedral angle
Distributed processing
Energy conservation
Open source software
Parameterization
Potential energy
Quantum mechanics
Recursive methods
Software packages
Source code
Wave fronts
Wave propagation
Workflow
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Summary:The parameterization of torsional/dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields. Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms. To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values. However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses. In this paper, we propose a systematic and versatile workflow called TorsionDrive to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development. The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described. The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.
ISSN:0021-9606
1089-7690
DOI:10.1063/5.0009232