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.

Neural networks to approach potential energy surfaces: Application to a molecular dynamics simulation

2007 ◽  
Vol 107 (11) ◽  
pp. 2120-2132 ◽  
Author(s):  
Diogo A. R. S. Latino ◽  
Filomena F. M. Freitas ◽  
João Aires-De-Sousa ◽  
Fernando M. S. Silva Fernandes
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Dynamics Simulation ◽  
Energy Surfaces

Neural networks as a tool for compact representation of ab initio molecular potential energy surfaces

1996 ◽  
Vol 14 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Erwin Tafeit ◽  
Willibald Estelberger ◽  
Renate Horejsi ◽  
Reinhard Moeller ◽  
Karl Oettl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Potential Energy ◽  
Ab Initio ◽  
Potential Energy Surfaces ◽  
Compact Representation ◽  
Molecular Potential ◽  
Molecular Potential Energy ◽  
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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