Full-dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide a means for accurate and efficient molecular simulations in the gas and condensed phase for various experimental observables ranging from spectroscopy to reaction dynamics. Here, the MLpot extension with PhysNet as the ML-based model for a PES is introduced into the newly developed pyCHARMM application programming interface. To illustrate the conception, validation, refining, and use of a typical workflow, para-chloro-phenol is considered as an example. The main focus is on how to approach a concrete problem from a practical perspective and applications to spectroscopic observables and the free energy for the –OH torsion in solution are discussed in detail. For the computed IR spectra in the fingerprint region, the computations for para-chloro-phenol in water are in good qualitative agreement with experiment carried out in CCl4. Moreover, relative intensities are largely consistent with experimental findings. The barrier for rotation of the –OH group increases from kcal/mol in the gas phase to kcal/mol from simulations in water due to favorable H-bonding interactions of the –OH group with surrounding water molecules.
PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations
Note: This paper is part of the JCP Special Topic on Software for Atomistic Machine Learning.
Kaisheng Song, Silvan Käser, Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly; PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations. J. Chem. Phys. 14 July 2023; 159 (2): 024125. https://doi.org/10.1063/5.0155992
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