Calculation Method of Total Energy and Atomic Interaction Potential Through Machine Learing Using a Neural Network of Atomic Structure Data

2020 ◽  
Vol 70 (5) ◽  
pp. 398-404
Author(s):  
Doukyun KIM ◽  
Chul Hong PARK*
Keyword(s):  
Neural Network ◽  
Total Energy ◽  
Structure Data ◽  
Interaction Potential ◽  
Atomic Structure ◽  
Calculation Method ◽  
Atomic Interaction

ATOMIC STRUCTURE AND ENERGETICS OF VACANCIES IN A SUBLAYER OF GaAs(110)

2000 ◽  
Vol 07 (01n02) ◽  
pp. 55-59
Author(s):  
JAE-YEL YI ◽  
JA-YONG KOO ◽  
SEKYUNG LEE ◽  
DONG-HYUK SHIN ◽  
JEONG SOOK HA
Keyword(s):  
Total Energy ◽  
Ab Initio ◽  
Atomic Structure ◽  
Calculation Method ◽  
Structural Changes ◽  
Energy Calculation ◽  
Total Energy Calculation

The energetics and structure of vacancies in the second layer of GaAs(110) were examined using the ab initio total energy calculation method. Structural changes due to the presence of the vacancies are substantial. The changes are different in shape, depending on the type of vacancy and charged states. Both the Ga vacancy and the As vacancy in the second layer of the surface favor negatively charged states.

scholarly journals CONSTRUCTION OF NEURAL NETWORK MODEL FOR STUDYING OF CRYSTAL STRUCTURES PROPERTIES

2021 ◽  
Author(s):  
Olga Uvarova ◽  
Sergey Uvarov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Total Energy ◽  
Crystal Structures ◽  
Network Model ◽  
Neural Network Model ◽  
Cohesive Energy ◽  
Computational Experiment ◽  
Model Based ◽  
Artificial Neural

The paper considers a mechanism for constructing a model based on artificial neural network for obtaining the values of the cohesive energy of a system of atoms. Cohesive energy allows for calculation of total energy of system. It is one of the most important characteristics of a structure. A computational experiment is carried out for one-component crystal structures of Si, Ge and C.

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