Database Screening of Ternary Chalcogenides for P-type Transparent Conductors

Raghupathy, Ramya Kormath Madam; Wiebeler, Hendrik; Kühne, Thomas D.; Felser, Claudia; Mirhosseini, Hossein

doi:10.1021/acs.chemmater.8b02719

Cited by 37 publications

(19 citation statements)

References 54 publications

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“…The data set of conductivity effective mass developed within the Materials Project and used in several screenings has been recently released by Ricci et al 23 A very good demonstration of how data sharing can rapidly be used is provided by the work from Raghupathy et al who very quickly used this data set to perform a HT search for p-type ternary chalcogenides. 110 The authors screened for stability, band gap and hole conductivity effective mass from the shared data set. After investigating the dopability, they finally listed a series of potential p-type TCMs highlighting IrSbS.…”

Section: Ht Approachmentioning

confidence: 99%

Transparent conducting materials discovery using high-throughput computing

et al. 2019

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Transparent conducting materials (TCMs) are required in many applications from solar cells to transparent electronics. Developing high performance materials combining the antagonistic properties of transparency and conductivity has been challenging especially for p-type materials. Recently, high-throughput ab initio computational screening has emerged as a formidable tool for accelerating materials discovery. In this review, we discuss how this approach has been applied for identifying TCMs. We provide a brief overview of the different materials properties of importance for TCMs (e.g., dopability, effective mass, and transparency) and present the ab initio techniques available to assess them. We focus on the accuracy of the methodologies as well as their suitability for high-throughput computing. Finally, we review the different high-throughput computational studies searching for new TCMs and discuss their differences in terms of methodologies and main findings.

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Section: Ht Approachmentioning

confidence: 99%

Transparent conducting materials discovery using high-throughput computing

et al. 2019

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“…In recent years, a wealth of literature is focused on HT computational screening and discovery of p-type TCMs. 25,[157][158][159][160][161][162][163][164][165][166] Especially, a set of 18 valence electron ternary ABX compounds (with 1:1:1 stoichiometry) were identified as promising p-type TCMs. 37,167 Based on the HT DFT calculations, Ran et al 168 potential p-type TCOs, among which the p-type dopability was found to be promisingly feasible for the compounds with larger n value.…”

Section: Transparent Conducting Materialsmentioning

confidence: 99%

“…The TCMs can be classified into two types, namely p‐type and n‐type. In recent years, a wealth of literature is focused on HT computational screening and discovery of p‐type TCMs 25,157–166 . Especially, a set of 18 valence electron ternary ABX compounds (with 1:1:1 stoichiometry) were identified as promising p‐type TCMs 37,167 .…”

Section: Ht Computational Materials Screening and Discovery Of Optoelmentioning

confidence: 99%

High‐throughput computational materials screening and discovery of optoelectronic semiconductors

Luo

Wang

et al. 2020

WIREs Comput Mol Sci

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In the recent past, optoelectronic semiconductors have attracted significant research attention both experimentally and theoretically toward large-scale applications in energy conversion, lighting, imaging, detection, and so on. With advancement in computing power and rapid development of computational algorithms, scientific community resorts to materials simulation to explore the hidden potential behind thousands of potentially unknown materials within short timeframes that the real experiments might take a long time. Within this context, the high-throughput (HT) computational materials screening has emerged as a useful tool to accelerate materials discovery, especially in the field of optoelectronic semiconductors. One of the important consequences is the construction of a number of material databases containing wide range of functional materials with their diverse physical properties and applications. Herein, we reviewed the recent progress on HT computational screening of optoelectronic semiconductors, with focus on photovoltaic solar absorbers, photoelectrochemical cells, semiconductor light-emitting diodes, and transparent conducting materials. We have also summarized the general workflow of HT computational screening, released workhorse models, and existing material databases. Finally, we offer perspectives for future research with a hope that this study could inspire new ideas for computational-driven optoelectronic semiconductor discovery in the HT routine. This article is categorized under: Structure and Mechanism > Computational Materials Science K E Y W O R D S first principles calculations, high-throughput, materials by design, optoelectronic semiconductors 1 | INTRODUCTION The excessive use of fossil fuels has caused global energy crisis and environmental concerns at the current stage. 1 Finding and designing novel energy materials is the key to sustainable development of human society. 2-4 Additionally, the information and electronic devices are widely used in industrials, astronautic, environmental, medical, and biological fields. 5 Materials with low cost and superior electroluminescence performance are the metrics for the development of

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“…We note that none of the materials exhibit intrinsic p-type behavior. This observation indicates that the challenge of finding naturally pdoped semiconductors/insulators, which is well known for bulk materials [76,77,78,79], carries over to the class of 2D materials.…”

Section: Intrinsic Carrier Concentrationsmentioning

confidence: 86%

Quantum point defects in 2D materials: The QPOD database

Bertoldo¹,

Ali²,

Manti³

et al. 2021

Preprint

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Atomically thin two-dimensional (2D) materials are ideal host systems for quantum defects as they offer easier characterisation, manipulation and read-out of defect states as compared to their bulk counterparts. Here we introduce the Quantum Point Defect (QPOD) database with more than 1900 defect systems comprising various charge states of 503 intrinsic point defects (vacancies and antisites) in 82 different 2D semiconductors and insulators. The Atomic Simulation Recipes (ASR) workflow framework was used to perform density functional theory (DFT) calculations of defect formation energies, charge transition levels, Fermi level positions, equilibrium defect and carrier concentrations, transition dipole moments, hyperfine coupling, and zero-field splitting. Excited states and photoluminescence spectra were calculated for selected high-spin defects. In this paper we describe the calculations and workflow behind the QPOD database, present an overview of its content, and discuss some general trends and correlations in the data. We analyse the degree of defect tolerance as well as intrinsic dopability of the host materials and identify promising defects for quantum technological applications. The database is freely available and can be browsed via a web-app interlinked with the Computational 2D Materials Database (C2DB).

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Database Screening of Ternary Chalcogenides for P-type Transparent Conductors

Cited by 37 publications

References 54 publications

Transparent conducting materials discovery using high-throughput computing

Transparent conducting materials discovery using high-throughput computing

High‐throughput computational materials screening and discovery of optoelectronic semiconductors

Quantum point defects in 2D materials: The QPOD database

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