Ground state potential energy curves for He2, Ne2, Ar2, He–Ne, He–Ar, and Ne–Ar: A coupled-cluster study

1999 ◽  
Vol 111 (23) ◽  
pp. 10520-10528 ◽  
Author(s):  
Sławomir M. Cybulski ◽  
Rafał R. Toczyłowski
Keyword(s):  
Potential Energy ◽  
Ground State ◽  
Potential Energy Curves ◽  
Coupled Cluster ◽  
State Potential

Ab initio MC–SCF ground‐state potential energy curves for LiH−, NaH−, and CsH−

1978 ◽  
Vol 68 (4) ◽  
pp. 1942-1950 ◽  
Author(s):  
Arnold M. Karo ◽  
Maureen A. Gardner ◽  
John R. Hiskes
Keyword(s):  
Potential Energy ◽  
Ab Initio ◽  
Ground State ◽  
Potential Energy Curves ◽  
State Potential

The ground-state potential energy curve of the radium dimer from relativistic coupled cluster calculations

2015 ◽  
Vol 143 (8) ◽  
pp. 084307 ◽  
Author(s):  
Tiago Quevedo Teodoro ◽  
Roberto Luiz Andrade Haiduke ◽  
Umakanth Dammalapati ◽  
Steven Knoop ◽  
Lucas Visscher
Keyword(s):  
Potential Energy ◽  
Ground State ◽  
Potential Energy Curve ◽  
Coupled Cluster ◽  
Energy Curve ◽  
Cluster Calculations ◽  
State Potential ◽  
Coupled Cluster Calculations

scholarly journals Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 828
Author(s):  
Rongxin Xia ◽  
Sabre Kais
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Ground State ◽  
Potential Energy Surfaces ◽  
Potential Energy Curves ◽  
The Neural Network ◽  
State Potential ◽  
Molecular Potential Energy ◽  
Simple Molecules ◽  
Energy Surfaces

We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H2, LiH, and BeH2. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.

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