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Title: Properties of AgBiI 4 using high through-put DFT and machine learning methods
Silver iodo-bismuthates show promise for optoelectronic and other applications. Within this family of materials, AgBiI 4 is a prominent model compound. The complexity of AgBiI 4 has prevented a conclusive determination of specific atomic arrangements of metal atoms in the bulk material. Here, we employ high through-put density functional and novel machine learning methods to determine physically relevant unit cell configurations. We also calculate the fundamental properties of the bulk material using newly discovered configurations. Our results for the lattice constant (12.7 Å) and bandgap (1.8 eV) agree with the previous theory and experiment. We report new predictions for the bulk modulus (7.5 GPa) and the temperature-dependent conductivity mass for electrons ([Formula: see text] at T = 300 K) and holes (7[Formula: see text] at T = 300 K); these masses will be useful in AgBiI 4 -based device simulations.  more » « less
Award ID(s):
2127473
PAR ID:
10402484
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Journal of Applied Physics
Volume:
131
Issue:
24
ISSN:
0021-8979
Page Range / eLocation ID:
245701
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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