Enhanced Sampling Path Integral Methods Using Neural Network Potential Energy Surfaces with Application to Diffusion in Hydrogen Hydrates. Issue 4 (18th December 2020)

• Record Type: Journal Article
• Authors: Cendagorta, Joseph R.; Shen, Hengyuan; Bačić, Zlatko; Tuckerman, Mark E.
• Is Part Of: Advanced theory and simulations. Volume 4:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-18
• Subjects: clathrate hydrates -- enhanced sampling -- machine learning -- neural networks -- path integrals; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202000258
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: The high cost of ab initio molecular dynamics (AIMD) simulations to model complex physical and chemical systems limits its ability to address many key questions. However, new machine learning‐based representations of complex potential energy surfaces have been introduced in recent years to circumvent computationally demanding AIMD simulations while retaining the same level of accuracy. As these machine learning methods gain in popularity over the next decade, it is important to address the appropriate way to develop and integrate them with well‐established simulation methods. This paper details the parameterization and training, using accurate electronic structure calculations, of artificial neural network potentials (NNPs) to model the intermolecular interactions in a hydrogen clathrate hydrate, wherein hydrogen molecules are confined inside the cavities of the 3D crystalline framework of hydrogen‐bonded water molecules. This new NNP is used in conjunction with new path‐integral based enhanced sampling methods, for inclusion of nuclear quantum effects and promotion of free energy barrier crossing, in order to determine properties that are important for understanding the diffusion of hydrogen gas in the clathrate system. These simulations demonstrate the influence of cage occupancy on the free energy barriers that determine the diffusivity of hydrogen gas through the network of large cages. Abstract : A neural network potential (NNP) using atomic fingerprints models the intermolecular interactions between water and hydrogen molecules in an sII hydrogen clathrate hydrate. Forces and energies from the NNP are used in path‐integral‐based enhanced sampling methods to determine properties that are important for understanding the diffusion of hydrogen gas in the clathrate system.