<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Wang, Zhiqi</dc:creator> <dc:creator>Gong, Yutong</dc:creator> <dc:creator>Evans, Matthew L.</dc:creator> <dc:creator>Yan, Yujing</dc:creator> <dc:creator>Wang, Shiyao</dc:creator> <dc:creator>Miao, Nanxi</dc:creator> <dc:creator>Zheng, Ruiheng</dc:creator> <dc:creator>Rignanese, Gian-Marco</dc:creator> <dc:creator>Wang, Junjie</dc:creator> <dc:date>2023-11-28</dc:date> <dc:description>This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A₂BC₂ family of compounds with the P4/mbm space group. Starting from a library of 214 known A₂BC₂ phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2023.181</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:c8-gy</dc:identifier> <dc:identifier>mcid:2023.181</dc:identifier> <dc:identifier>oai:materialscloud.org:1956</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>density-functional theory</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>electrides</dc:subject> <dc:title>Machine learning-accelerated discovery of A₂BC₂ ternary electrides with diverse anionic electron densities</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>