On the representation of many-body interactions in water

Abstract

Recent work has shown that the many-body expansion of the interaction energy can effectively be used to develop analytical representations of global potential energy surfaces (PESs) for water. In this study, the role of short- and long-range contri- butions at different orders is investigated by analyzing water potentials that treat the leading terms of the many-body expansion through implicit (i.e., TTM3-F and TTM4-F PESs) and explicit (i.e., WHBB and MB-pol PESs) representations. It is found that explicit short-range representations of 2-body and 3-body interactions along with a physically correct integration of short- and long-range contributions are necessary for an accurate representation of the water interactions from the gas to the condensed phase. Similarly, a complete many-body representation of the dipole moment surface is found to be crucial to reproducing the correct intensities of the infrared spectrum of liquid water.

References in corpus (3)

• Competing quantum effects in the dynamics of a flexible water model
• On the accuracy of DFT exchange-correlation functionals for H bonds in small water clusters II: The water hexamer and van der Waals interactions
• Assessing the accuracy of quantum Monte Carlo and density functional theory for energetics of small water clusters

Cited by in corpus (16)

• Physics-inspired structural representations for molecules and materials
• On the accuracy of the MB-pol many-body potential for water: Interaction energies, vibrational frequencies, and classical thermodynamic and dynamical properties from clusters to liquid water and ice
• Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
• The Many-Body Expansion Combined with Neural Networks
• Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment
• Mass Density Fluctuations in Quantum and Classical descriptions of Liquid Water
• Probing defects and correlations in the hydrogen-bond network of ab initio water
• Inexpensive modelling of quantum dynamics using path integral generalized Langevin equation thermostats
• A CCSD(T)-based permutationally invariant polynomial 4-body potential for water
• Critical role of quantum dynamical effects in the Raman spectroscopy of liquid water
• Optimization of an exchange-correlation density functional for water
• A $\Delta$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential
• Improved Ab Initio Molecular Dynamics by Minimal Biasing with Experimental Data
• Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
• Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water
• A neural network-based four-body potential energy surface for parahydrogen