scholarly journals New insight into the potential energy landscape and relaxation pathways of photoexcited aniline from CASSCF and XMCQDPT2 electronic structure calculations

2014 ◽  
Vol 16 (7) ◽  
pp. 3122-3133 ◽  
Author(s):  
Matthieu Sala ◽  
Oliver M. Kirkby ◽  
Stéphane Guérin ◽  
Helen H. Fielding
Keyword(s):  
Electronic Structure ◽  
Potential Energy ◽  
Excited States ◽  
Energy Landscape ◽  
Electronic Structure Calculations ◽  
Relaxation Dynamics ◽  
Potential Energy Landscape ◽  
Structure Calculations ◽  
Insight Into

New insight into the nonadiabatic relaxation dynamics of aniline following excitation to its first three singlet excited states, 11ππ*, 11π3s/πσ* and 21ππ*.

A Molecular-Level Mechanism of the Biological N2 Fixation

2019 ◽  
Author(s):  
Vanessa Jane Bukas ◽  
Jens Kehlet Nørskov
Keyword(s):  
Electronic Structure ◽  
N2 Fixation ◽  
Molecular Level ◽  
Electronic Structure Calculations ◽  
Atmospheric Nitrogen ◽  
Biological Fixation ◽  
Biological N2 Fixation ◽  
Electrochemical Processes ◽  
Structure Calculations ◽  
Insight Into

We present a detailed molecular-level mechanism for the biological fixation of atmospheric nitrogen into ammonia. The mechanism is based on a series of electronic structure calculations and provides insight into the key question of what it is that the enzyme does to enable selective N<sub>2</sub> reduction that cannot be mimicked by simple electrochemical processes.

Active Learning of Many-Body Configuration Space: Application to the Cs+–water MB-nrg Potential Energy Function as a Case Study

2020 ◽  
Author(s):  
Yaoguang Zhai ◽  
Alessandro Caruso ◽  
Sicun Gao ◽  
Francesco Paesani
Keyword(s):  
Electronic Structure ◽  
Active Learning ◽  
Potential Energy ◽  
Molecular Simulations ◽  
Electronic Structure Calculations ◽  
Many Body ◽  
High Level ◽  
Structure Calculations ◽  
Training Sets

<div> <div> <div> <p>The efficient selection of representative configurations that are used in high-level electronic structure calculations needed for the development of many-body molecular models poses a challenge to current data-driven approaches to molecular simulations. Here, we introduce an active learning (AL) framework for generating training sets corresponding to individual many-body contributions to the energy of a N-body system, which are required for the development of MB-nrg potential energy functions (PEFs). Our AL framework is based on uncertainty and error estimation, and uses Gaussian process regression (GPR) to identify the most relevant configurations that are needed for an accurate representation of the energy landscape of the molecular system under exam. Taking the Cs<sup>+</sup>–water system as a case study, we demonstrate that the application of our AL framework results in significantly smaller training sets than previously used in the development of the original MB-nrg PEF, without loss of accuracy. Considering the computational cost associated with high-level electronic structure calculations for training set configurations, our AL framework is particularly well-suited to the development of many-body PEFs, with chemical and spectroscopic accuracy, for molecular simulations from the gas to condensed phase. </p> </div> </div> </div>

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