skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system

Abstract

Here, the permutation invariant polynomial-neural network (PIP-NN) approach is extended to fit intermolecular potential energy surfaces (PESs). Specifically, three PESs were constructed for the Ne-C2H2 system. PES1 is a full nine-dimensional PIP-NN PES directly fitted to ~42 000 ab initio points calculated at the level of CCSD(T)-F12a/cc-pCVTZ-F12, while the other two consist of the six-dimensional PES for C2H2 [H. Han, A. Li, and H. Guo, J. Chem. Phys. 141, 244312 (2014)] and an intermolecular PES represented in either the PIP (PES2) or PIP-NN (PES3) form. The comparison of fitting errors and their distributions, one-dimensional cuts and two-dimensional contour plots of the PESs, as well as classical trajectory collisional energy transfer dynamics calculations shows that the three PESs are very similar. We conclude that full-dimensional PESs for non-covalent interacting molecular systems can be constructed efficiently and accurately by the PIP-NN approach for both the constituent molecules and intermolecular parts.

Authors:
 [1];  [2]
  1. Chongqing Univ., Chongqing (China)
  2. Univ. of New Mexico, Albuquerque, NM (United States)
Publication Date:
Research Org.:
Univ. of New Mexico, Albuquerque, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1468464
Alternate Identifier(s):
OSTI ID: 1227589
Grant/Contract Number:  
FG02-05ER15694
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 143; Journal Issue: 21; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS

Citation Formats

Li, Jun, and Guo, Hua. Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system. United States: N. p., 2015. Web. doi:10.1063/1.4936660.
Li, Jun, & Guo, Hua. Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system. United States. https://doi.org/10.1063/1.4936660
Li, Jun, and Guo, Hua. 2015. "Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system". United States. https://doi.org/10.1063/1.4936660. https://www.osti.gov/servlets/purl/1468464.
@article{osti_1468464,
title = {Permutationally invariant fitting of intermolecular potential energy surfaces: A case study of the Ne-C2H2 system},
author = {Li, Jun and Guo, Hua},
abstractNote = {Here, the permutation invariant polynomial-neural network (PIP-NN) approach is extended to fit intermolecular potential energy surfaces (PESs). Specifically, three PESs were constructed for the Ne-C2H2 system. PES1 is a full nine-dimensional PIP-NN PES directly fitted to ~42 000 ab initio points calculated at the level of CCSD(T)-F12a/cc-pCVTZ-F12, while the other two consist of the six-dimensional PES for C2H2 [H. Han, A. Li, and H. Guo, J. Chem. Phys. 141, 244312 (2014)] and an intermolecular PES represented in either the PIP (PES2) or PIP-NN (PES3) form. The comparison of fitting errors and their distributions, one-dimensional cuts and two-dimensional contour plots of the PESs, as well as classical trajectory collisional energy transfer dynamics calculations shows that the three PESs are very similar. We conclude that full-dimensional PESs for non-covalent interacting molecular systems can be constructed efficiently and accurately by the PIP-NN approach for both the constituent molecules and intermolecular parts.},
doi = {10.1063/1.4936660},
url = {https://www.osti.gov/biblio/1468464}, journal = {Journal of Chemical Physics},
issn = {0021-9606},
number = 21,
volume = 143,
place = {United States},
year = {Wed Dec 02 00:00:00 EST 2015},
month = {Wed Dec 02 00:00:00 EST 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 6 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Toward spectroscopically accurate global ab initio potential energy surface for the acetylene-vinylidene isomerization
journal, December 2014


A simple and efficient CCSD(T)-F12 approximation
journal, December 2007


Permutationally invariant potential energy surfaces in high dimensionality
journal, October 2009


“Plug and play” full-dimensional ab initio potential energy and dipole moment surfaces and anharmonic vibrational analysis for CH 4 –H 2 O
journal, January 2015


Potential Energy Surfaces Fitted by Artificial Neural Networks
journal, March 2010


Molecular potential energy surfaces by interpolation
journal, June 1994


High-dimensional ab initio potential energy surfaces for reaction dynamics calculations
journal, January 2011


Vibrational Energy Transfer
journal, January 1996


Global ab initio ground-state potential energy surface of N 4
journal, July 2013


Theoretical Unimolecular Kinetics for CH 4 + M ⇄ CH 3 + H + M in Eight Baths, M = He, Ne, Ar, Kr, H 2 , N 2 , CO, and CH 4
journal, June 2011


Quasiclassical trajectory studies using 3D spline interpolation of a b i n i t i o surfaces
journal, July 1975


Master Equation Analysis of Pressure-Dependent Atmospheric Reactions
journal, December 2003


Dynamics Study of the Reaction Ar + HCN → Ar + H + CN
journal, July 1998


Predictive a priori pressure-dependent kinetics
journal, December 2014


High-Level, First-Principles, Full-Dimensional Quantum Calculation of the Ro-vibrational Spectrum of the Simplest Criegee Intermediate (CH 2 OO)
journal, June 2014


On-the-fly ab intito calculations of anharmonic vibrational frequencies: Local-monomer theory and application to HCl clusters
journal, October 2013


Direct Dynamics Simulations
journal, July 2003


Trajectory simulations of collisional energy transfer in highly excited benzene and hexafluorobenzene
journal, July 1995


Correlation consistent basis sets for molecular core-valence effects with explicitly correlated wave functions: The atoms B–Ne and Al–Ar
journal, February 2010


Beyond the Lennard-Jones model: a simple and accurate potential function probed by high resolution scattering data useful for molecular dynamics simulations
journal, January 2008


Simplified CCSD(T)-F12 methods: Theory and benchmarks
journal, February 2009


Intermolecular vibrational energy transfer in thermal unimolecular systems
journal, June 1977


Classical Trajectory Study of Energy Transfer in Collisions of Highly Excited Allyl Radical with Argon
journal, December 2013


C ONSTRUCTING M ULTIDIMENSIONAL M OLECULAR P OTENTIAL E NERGY S URFACES FROM A B I NITIO D ATA
journal, October 1999


Strong combination-band IR emission from highly vibrationally excited acetylene
journal, January 2010


Vectorization of the general Monte Carlo classical trajectory program VENUS
journal, October 1991


Permutation invariant polynomial neural network approach to fitting potential energy surfaces
journal, August 2013


Permutationally Invariant Fitting of Many-Body, Non-covalent Interactions with Application to Three-Body Methane–Water–Water
journal, March 2015


Molecular potential-energy surfaces for chemical reaction dynamics
journal, December 2002


Interpolating moving least-squares methods for fitting potential energy surfaces: A strategy for efficient automatic data point placement in high dimensions
journal, February 2008


Energy transfer in highly excited large polyatomic molecules
journal, December 1990


Collisional Energy Transfer in Unimolecular Reactions: Direct Classical Trajectories for CH 4 ⇄ CH 3 + H in Helium
journal, May 2009


Effects of Zero-Point Delocalization on the Vibrational Frequencies of Mixed HCl and Water Clusters
journal, June 2014


Intermolecular potential effects in trajectory calculations of collisions between large highly excited molecules and noble gases
journal, December 1996


Chemical Activation through Super Energy Transfer Collisions
journal, January 2014


Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems
journal, November 2013


Energy dependence of energy transfer in the collisional relaxation of vibrationally highly excited carbon disulfide
journal, October 1991


Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
journal, January 2011


An improved simple model for the van der Waals potential based on universal damping functions for the dispersion coefficients
journal, April 1984


Accurate ab initio and “hybrid” potential energy surfaces, intramolecular vibrational energies, and classical ir spectrum of the water dimer
journal, April 2009


Collision Efficiency of Water in the Unimolecular Reaction CH 4 (+H 2 O) ⇆ CH 3 + H (+H 2 O): One-Dimensional and Two-Dimensional Solutions of the Low-Pressure-Limit Master Equation
journal, November 2013


Communication: A benchmark-quality, full-dimensional ab initio potential energy surface for Ar-HOCO
journal, April 2014


Full-dimensional, ab initio potential energy and dipole moment surfaces for water
journal, January 2009


Monte carlo sampling of a microcanonical ensemble of classical harmonic oscillators
journal, September 1980


Trajectory Study of Supercollision Relaxation in Highly Vibrationally Excited Pyrazine and CO 2
journal, September 2005


Quantum Dynamics of Vinylidene Photodetachment on an Accurate Global Acetylene-Vinylidene Potential Energy Surface
journal, July 2015


From ab Initio Potential Energy Surfaces to State-Resolved Reactivities: X + H 2 O ↔ HX + OH [X = F, Cl, and O( 3 P)] Reactions
journal, May 2015


Ab Initio Wavenumber Accurate Spectroscopy: 1 CH 2 and HCN Vibrational Levels on Automatically Generated IMLS Potential Energy Surfaces
journal, April 2009


Works referencing / citing this record:

Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks
journal, June 2016


Energy landscapes for machine learning
text, January 2017