PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations

TL;DR

This paper introduces a machine learning-based potential energy surface framework integrated into pyCHARMM, demonstrated on para-chloro-phenol, showing accurate spectroscopic and free energy predictions in solution.

Contribution

It presents the integration of PhysNet-based ML potentials into pyCHARMM for routine molecular simulations, enabling practical workflows and validation against experimental data.

Findings

IR spectra in water agree qualitatively with experiments

Rotation barrier of -OH increases in water due to H-bonding

Workflow demonstrates accurate free energy and spectroscopic predictions

Abstract

Full dimensional potential energy surfaces (PESs) based on machine learning (ML) techniques provide 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 API. To illustrate conceiving, validating, refining and using 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 CCl$_4$. Also, relative intensities are largely consistent with experimental findings. The barrier for rotation of the -OH group increases from $\sim 3.5$ kcal/mol in the gas phase to $\sim 4.1$ kcal/mol from simulations in water due to favourable H-bonding interactions of the -OH group with surrounding water molecules.