Development of a Structural Comparison Method to Promote Exploration of the Potential Energy Surface in the Global Optimization of Nanoclusters

2021 ◽  
Author(s):  
Geoffrey R. Weal ◽  
Samantha M. McIntyre ◽  
Anna L. Garden
Keyword(s):  
Global Optimization ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Comparison Method ◽  
Structural Comparison

scholarly journals Atomic structure of boron resolved using machine learning and global sampling

2018 ◽  
Vol 9 (46) ◽  
pp. 8644-8655 ◽  
Author(s):  
Si-Da Huang ◽  
Cheng Shang ◽  
Pei-Lin Kang ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Atomic Structure ◽  
Energy Surface ◽  
First Time

Here, by combining machine learning with the latest stochastic surface walking (SSW) global optimization, we explore for the first time the potential energy surface of β-B.

scholarly journals Thermodynamic rules for zeolite formation from machine learning based global optimization

2020 ◽  
Vol 11 (37) ◽  
pp. 10113-10118
Author(s):  
Sicong Ma ◽  
Cheng Shang ◽  
Chuan-Ming Wang ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Atomic Simulation ◽  
Zeolite Formation ◽  
Zeolite P

Machine learning based atomic simulation explores more than one million minima from global potential energy surface of SiAlPO system, and identifies thermodynamics rules on energetics, framework and composition for stable zeolite.

An accurate, global, ab initio potential energy surface for the H + 3 molecule

2000 ◽  
Vol 98 (5) ◽  
pp. 261-273 ◽  
Author(s):  
Oleg L. Polyansky, Rita Prosmiti, Wim Kl
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Ab Initio ◽  
Energy Surface

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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