scholarly journals Inclusion of Relativistic Effects in Electronic Structure Calculations

1986 ◽  
Vol 39 (5) ◽  
pp. 667 ◽  
Author(s):  
KG Dyall
Keyword(s):  
Molecular Structure ◽  
Electronic Structure ◽  
Dirac Equation ◽  
Atomic Number ◽  
Perturbation Methods ◽  
Relativistic Effects ◽  
Electronic Structure Calculations ◽  
Variational Treatment ◽  
Structure Calculations

The effects of relativity on atomic and molecular structure are discussed with an indication of their importance as a function of atomic number. Perturbation methods for the inclusion of relativistic effects are briefly analysed in terms of the Dirac equation; the multi-configuration Dirac-Fock method for the variational treatment of relativistic effects is then discussed in more detail. Finally, a case study on 2p ionisation in Ca is presented, in which higher-order relativistic effects are important.

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>

scholarly journals 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>

Can one use the electronic absorption spectra of metalloporphyrins to benchmark electronic structure methods? A case study on the cobalt porphyrin

2020 ◽  
Vol 22 (41) ◽  
pp. 23886-23898
Author(s):  
Jhonathan Rosa de Souza ◽  
Matheus Morato F. de Moraes ◽  
Yuri Alexandre Aoto ◽  
Paula Homem-de-Mello
Keyword(s):  
Electronic Structure ◽  
Absorption Spectra ◽  
Electronic Absorption ◽  
Electronic Absorption Spectra ◽  
Electronic Structure Calculations ◽  
Cobalt Porphyrin ◽  
Electronic Structure Methods ◽  
Structure Calculations

One must be skeptical about the reference chosen to benchmark electronic structure calculations, such as DFT functionals and active spaces for multireference calculations.

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