scholarly journals Erratum: “Investigating bonding in small silicon–carbon clusters: Exploration of the potential energy surfaces of Si3C4, Si4C3, and Si4C4 using ab initio molecular dynamics” [J. Chem. Phys. 120, 4333 (2004)]

2004 ◽  
Vol 120 (22) ◽  
pp. 10853-10853 ◽  
Author(s):  
Marjorie Bertolus ◽  
Fabio Finocchi ◽  
Philippe Millié
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Chem Phys ◽  
Ab Initio Molecular Dynamics ◽  
Carbon Clusters ◽  
Silicon Carbon ◽  
Energy Surfaces

Erratum: “Ab initio computed diabatic potential energy surfaces of OH–HCl” [J. Chem. Phys. 122, 244325 (2005)]

2007 ◽  
Vol 126 (7) ◽  
pp. 079902 ◽  
Author(s):  
Paul E. S. Wormer ◽  
Jacek A. Kłos ◽  
Gerrit C. Groenenboom ◽  
Ad van der Avoird
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Chem Phys ◽  
Energy Surfaces

CALCULATION OF HO2 DENSITY OF STATES ON THREE POTENTIAL ENERGY SURFACES

2010 ◽  
Vol 09 (03) ◽  
pp. 653-665 ◽  
Author(s):  
H. ZHANG ◽  
S. C. SMITH
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Density Of States ◽  
Potential Energy Surfaces ◽  
Chem Phys ◽  
Many Body ◽  
Filter Diagonalization ◽  
Harmonic Density ◽  
Energy Surfaces ◽  
Semi Empirical

Density of states (DOS) in both bound and unimolecular dissociation regime for HO2 system have been calculated quantum mechanically by Lanczos homogeneous filter diagonalization (LHFD) method. Three potential energy surfaces are explored and the results are contrasted for the total angular momentum J = 0 density of states. While two ab initio potential energy surfaces (PESs) (TU PES, J Chem Phys, 115:3621 and XXZLG PES, J Chem Phys122:244) produce the DOSs which are in fairly good agreement, the semi-empirical double many-body expansion (DMBE) IV PES (J Phys Chem94:8073) generates the much higher DOSs in higher energy range. The quantum mechanical DOSs are also compared with Troe et al.'s results from harmonic density, semiclassical density and their early density of states on the same TU ab initio surface.

scholarly journals Raman spectrum and polarizability of liquid water from deep neural networks

2020 ◽  
Vol 22 (19) ◽  
pp. 10592-10602 ◽  
Author(s):  
Grace M. Sommers ◽  
Marcos F. Calegari Andrade ◽  
Linfeng Zhang ◽  
Han Wang ◽  
Roberto Car
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Raman Spectrum ◽  
Potential Energy ◽  
Ab Initio ◽  
Liquid Water ◽  
Potential Energy Surfaces ◽  
Deep Neural Networks ◽  
Energy Surfaces

Using deep neural networks to model the polarizability and potential energy surfaces, we compute the Raman spectrum of liquid water at several temperatures with ab initio molecular dynamics accuracy.

scholarly journals Publisher’s Note: “Ab initio potential energy surfaces for NH(Σ3−)−NH(Σ3−) with analytical long range” [J. Chem. Phys. 131, 224314 (2009)]

2010 ◽  
Vol 132 (2) ◽  
pp. 029902
Author(s):  
Liesbeth M. C. Janssen ◽  
Gerrit C. Groenenboom ◽  
Ad van der Avoird ◽  
Piotr S. Żuchowski ◽  
Rafał Podeszwa
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Long Range ◽  
Potential Energy Surfaces ◽  
Chem Phys ◽  
Energy Surfaces

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

2020 ◽  
Author(s):  
Shi Jun Ang ◽  
Wujie Wang ◽  
Daniel Schwalbe-Koda ◽  
Simon Axelrod ◽  
Rafael Gomez-Bombarelli
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Transition State ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface ◽  
Computational Cost ◽  
Reactive Trajectories ◽  
Dynamics Simulations ◽  
Energy Surfaces

<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>

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