Free energy profiles for chemical reactions in solution from high-dimensional neural network potentials: The case of the Strecker synthesis

Abstract

Machine learning potentials (MLPs) have become a popular tool in chemistry and materials science as they combine the accuracy of electronic structure calculations with the high computational efficiency of analytic potentials. MLPs are particularly useful for computationally demanding simulations such as the determination of free energy profiles governing chemical reactions in solution, but to date such applications are still rare. In this work we show how umbrella sampling simulations can be combined with active learning of high-dimensional neural network potentials (HDNNPs) to construct free energy profiles in a systematic way. For the example of the first step of Strecker synthesis of glycine in aqueous solution we provide a detailed analysis of the improving quality of HDNNPs for datasets of increasing size. We find that next to the typical quantification of energy and force errors with respect to the underlying density functional theory data also the long-term stability of the simulations and the convergence of physical properties should be rigorously monitored to obtain reliable and converged free energy profiles of chemical reactions in solution.

References in corpus (12)

• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Machine learning for molecular simulation
• Machine-learning based interatomic potential for amorphous carbon
• How van der Waals interactions determine the unique properties of water
• Tutorial: How to Train a Neural Network Potential
• Machine Learning Implicit Solvation for Molecular Dynamics
• Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
• Converged Colored Noise Path Integral Molecular Dynamics Study of the Zundel Cation down to Ultra-low Temperatures at Coupled Cluster Accuracy
• ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
• Intramolecular and water mediated tautomerism of solvated glycine
• Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water

Cited by in corpus (1)

• Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials