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Author Choudhary, K.; Bercx, M.; Jiang, J.; Pachter, R.; Lamoen, D.; Tavazza, F.
Title Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods Type A1 Journal article
Year (down) 2019 Publication Chemistry of materials Abbreviated Journal Chem Mater
Volume 31 Issue 15 Pages 5900-5908
Keywords A1 Journal article; Electron microscopy for materials research (EMAT)
Abstract Solar energy plays an important role in solving serious environmental

problems and meeting the high energy demand. However, the lack of suitable

materials hinders further progress of this technology. Here, we present the largest

inorganic solar cell material search till date using density functional theory (DFT) and

machine-learning approaches. We calculated the spectroscopic limited maximum

efficiency (SLME) using the Tran−Blaha-modified Becke−Johnson potential for 5097

nonmetallic materials and identified 1997 candidates with an SLME higher than 10%,

including 934 candidates with a suitable convex-hull stability and an effective carrier

mass. Screening for two-dimensional-layered cases, we found 58 potential materials

and performed G0W0 calculations on a subset to estimate the prediction uncertainty. As the above DFT methods are still computationally expensive, we developed a high accuracy machine-learning model to prescreen efficient materials and applied it to over a million materials. Our results provide a general framework and universal strategy for the design of high-efficiency solar

cell materials. The data and tools are publicly distributed at: https://www.ctcms.nist.gov/~knc6/JVASP.html, https://www.

ctcms.nist.gov/jarvisml/, https://jarvis.nist.gov/, and https://github.com/usnistgov/jarvis.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Wos 000480826900060 Publication Date 2019-08-13
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0897-4756 ISBN Additional Links UA library record; WoS full record; WoS citing articles
Impact Factor 9.466 Times cited 6 Open Access
Notes ; ; Approved Most recent IF: 9.466
Call Number EMAT @ emat @c:irua:161814 Serial 5291
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