scholarly journals Accelerated Discovery of Two-Dimensional Optoelectronic Octahedral Oxyhalides via High-Throughput Ab Initio Calculations and Machine Learning

2019 ◽  
Vol 10 (21) ◽  
pp. 6734-6740 ◽  
Author(s):  
Xing-Yu Ma ◽  
James P. Lewis ◽  
Qing-Bo Yan ◽  
Gang Su
Keyword(s):  
Machine Learning ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
High Throughput ◽  
Two Dimensional

scholarly journals Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yu Gan ◽  
Guanjie Wang ◽  
Jian Zhou ◽  
Zhimei Sun
Keyword(s):  
Machine Learning ◽  
Thermal Conductivity ◽  
Seebeck Coefficient ◽  
Ab Initio Calculations ◽  
Ab Initio ◽  
High Throughput ◽  
Network Models ◽  
Hole Doping ◽  
Neural Network Models ◽  
Family Based

AbstractLayered IV-V-VI semiconductors have immense potential for thermoelectric (TE) applications due to their intrinsically ultralow lattice thermal conductivity. However, it is extremely difficult to assess their TE performance via experimental trial-and-error methods. Here, we present a machine-learning-based approach to accelerate the discovery of promising thermoelectric candidates in this chalcogenide family. Based on a dataset generated from high-throughput ab initio calculations, we develop two highly accurate-and-efficient neural network models to predict the maximum ZT (ZTmax) and corresponding doping type, respectively. The top candidate, n-type Pb2Sb2S5, is successfully identified, with the ZTmax over 1.0 at 650 K, owing to its ultralow thermal conductivity and decent power factor. Besides, we find that n-type Te-based compounds exhibit a combination of high Seebeck coefficient and electrical conductivity, thereby leading to better TE performance under electron doping than hole doping. Whereas p-type TE performance of Se-based semiconductors is superior to n-type, resulting from large Seebeck coefficient induced by high density-of-states near valence band edges.

ChemInform Abstract: High-Throughput ab initio Screening for Two-Dimensional Electride Materials.

ChemInform ◽  
2014 ◽  
Vol 45 (48) ◽  
pp. no-no
Author(s):  
Tomofumi Tada ◽  
Seiji Takemoto ◽  
Satoru Matsuishi ◽  
Hideo Hosono
Keyword(s):  
Ab Initio ◽  
High Throughput ◽  
Two Dimensional

Searching for electronically two dimensional metals in high-throughput ab initio databases

2020 ◽  
Vol 182 ◽  
pp. 109747
Author(s):  
Matías Núñez ◽  
Ruben Weht ◽  
Manuel Núñez-Regueiro
Keyword(s):  
Ab Initio ◽  
High Throughput ◽  
Two Dimensional

scholarly journals Ab Initio Calculations of Ultrashort Carrier Dynamics in Two-Dimensional Materials: Valley Depolarization in Single-Layer WSe2

Nano Letters ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 4549-4555 ◽  
Author(s):  
Alejandro Molina-Sánchez ◽  
Davide Sangalli ◽  
Ludger Wirtz ◽  
Andrea Marini
Keyword(s):  
Ab Initio Calculations ◽  
Ab Initio ◽  
Single Layer ◽  
Carrier Dynamics ◽  
Two Dimensional ◽  
Two Dimensional Materials
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