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Title: Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction

Abstract

We report the computational investigation of a series of ternary X 4 Y 2 Z and X 5 Y 2 Z 2 compounds with X =Mg, Ca, Sr, Ba; Y =P, As, Sb, Bi; and Z =S, Se, Te. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on ab initio calculations, we predict that many of these phases are thermodynamically stable. In particular, 21 of the X 4 Y 2 Z compounds crystallize in a tetragonal structure with I ¯ 4 2 d symmetry, and exhibit band gaps in the range of 0.8 and 1.8 eV, well suited for various energy applications. We show that several candidates (in particular X 4 Y 2 Te and X 4 Sb 2 Se ) exhibit good photo absorption in the visible range, while others (e.g., Ba 4 Sb 2 Se ) show excellent thermoelectric performance due to high power factors and extremely low lattice thermal conductivities.

Authors:
 [1];  [2];  [2];  [3];  [2];  [2]
  1. Cornell Univ., Ithaca, NY (United States). Lab. of Atomic and Solid State Physics (LASSP)
  2. Northwestern Univ., Evanston, IL (United States). Dept. of Materials Science and Engineering
  3. Cornell Univ., Ithaca, NY (United States). Dept. of Chemistry and Chemical Biology
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory-National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE
OSTI Identifier:
1530175
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Physical Review Materials
Additional Journal Information:
Journal Volume: 3; Journal Issue: 3; Journal ID: ISSN 2475-9953
Country of Publication:
United States
Language:
English

Citation Formats

Amsler, Maximilian, Ward, Logan, Hegde, Vinay I., Goesten, Maarten G., Yi, Xia, and Wolverton, Chris. Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction. United States: N. p., 2019. Web. doi:10.1103/PhysRevMaterials.3.035404.
Amsler, Maximilian, Ward, Logan, Hegde, Vinay I., Goesten, Maarten G., Yi, Xia, & Wolverton, Chris. Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction. United States. doi:10.1103/PhysRevMaterials.3.035404.
Amsler, Maximilian, Ward, Logan, Hegde, Vinay I., Goesten, Maarten G., Yi, Xia, and Wolverton, Chris. Fri . "Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction". United States. doi:10.1103/PhysRevMaterials.3.035404.
@article{osti_1530175,
title = {Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction},
author = {Amsler, Maximilian and Ward, Logan and Hegde, Vinay I. and Goesten, Maarten G. and Yi, Xia and Wolverton, Chris},
abstractNote = {We report the computational investigation of a series of ternary X 4 Y 2 Z and X 5 Y 2 Z 2 compounds with X =Mg, Ca, Sr, Ba; Y =P, As, Sb, Bi; and Z =S, Se, Te. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on ab initio calculations, we predict that many of these phases are thermodynamically stable. In particular, 21 of the X 4 Y 2 Z compounds crystallize in a tetragonal structure with I ¯ 4 2 d symmetry, and exhibit band gaps in the range of 0.8 and 1.8 eV, well suited for various energy applications. We show that several candidates (in particular X 4 Y 2 Te and X 4 Sb 2 Se ) exhibit good photo absorption in the visible range, while others (e.g., Ba 4 Sb 2 Se ) show excellent thermoelectric performance due to high power factors and extremely low lattice thermal conductivities.},
doi = {10.1103/PhysRevMaterials.3.035404},
journal = {Physical Review Materials},
issn = {2475-9953},
number = 3,
volume = 3,
place = {United States},
year = {2019},
month = {3}
}