Strain-tuned properties of hybrid improper ferroelectric superlattices through first-principles calculations and machine learning

2020 ◽  
Vol 101 (5) ◽  
Author(s):  
Monirul Shaikh ◽  
Madhusudan Karmakar ◽  
Saurabh Ghosh
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
First Principles Calculations

Machine-Learning-Assisted First-Principles Calculations of Strained InAs1−xSbx Alloys for Curved Focal-Plane Arrays

2021 ◽  
Vol 15 (6) ◽  
Author(s):  
Alexandros Kyrtsos ◽  
John Glennon ◽  
Andreu Glasmann ◽  
Mark R. O’Masta ◽  
Binh-Minh Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Focal Plane ◽  
Focal Plane Arrays ◽  
First Principles Calculations

Fast and scalable prediction of local energy at grain boundaries: machine-learning based modeling of first-principles calculations

2017 ◽  
Vol 25 (7) ◽  
pp. 075003 ◽  
Author(s):  
Tomoyuki Tamura ◽  
Masayuki Karasuyama ◽  
Ryo Kobayashi ◽  
Ryuichi Arakawa ◽  
Yoshinori Shiihara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Grain Boundaries ◽  
First Principles ◽  
Local Energy ◽  
First Principles Calculations

Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations

2020 ◽  
Vol 12 (51) ◽  
pp. 56850-56861
Author(s):  
Camilo A. F. Salvador ◽  
Bruno F. Zornio ◽  
Caetano R. Miranda
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
First Principles Calculations ◽  
Low Modulus ◽  
Zr Alloys

scholarly journals Stepwise Evolution of Photocatalytic Spinel-Structured (Co,Cr,Fe,Mn,Ni)3O4 High Entropy Oxides from First-Principles Calculations to Machine Learning

Crystals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1035
Author(s):  
Chia-Chun Lin ◽  
Chia-Wei Chang ◽  
Chao-Cheng Kaun ◽  
Yen-Hsun Su
Keyword(s):  
Machine Learning ◽  
First Principles ◽  
Computing Time ◽  
First Principles Calculations ◽  
High Entropy ◽  
Lattice Constants ◽  
Fundamental Properties ◽  
Synthesis Properties ◽  
Stepwise Evolution ◽  
Formation Energies

High entropy oxides (HEOx) are novel materials, which increase the potential application in the fields of energy and catalysis. However, a series of HEOx is too novel to evaluate the synthesis properties, including formation and fundamental properties. Combining first-principles calculations with machine learning (ML) techniques, we predict the lattice constants and formation energies of spinel-structured photocatalytic HEOx, (Co,Cr,Fe,Mn,Ni)3O4, for stoichiometric and non-stoichiometric structures. The effects of site occupation by different metal cations in the spinel structure are obtained through first-principles calculations and ML predictions. Our predicted results show that the lattice constants of these spinel-structured oxides are composition-dependent and that the formation energies of those oxides containing Cr atoms are low. The computing time and computing energy can be greatly economized through the tandem approach of first-principles calculations and ML.

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