<?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>Kahle, Leonid</dc:creator> <dc:creator>Marcolongo, Aris</dc:creator> <dc:creator>Marzari, Nicola</dc:creator> <dc:date>2024-04-26</dc:date> <dc:description>We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start from ~1400 unique Li-containing materials, of which ~900 are insulators at the level of density-functional theory. For those, we calculate the diffusion coefficient in a highly automated fashion, using extensive molecular dynamics simulations on a potential energy surface (the recently published pinball model) fitted on first-principles forces. The ~130 most promising candidates are studied with full first-principles molecular dynamics, first at high temperature and then more extensively for the 78 most promising candidates. The results of the first-principles simulations of the candidate solid-state electrolytes found are discussed in detail. Update April 2024: Files are added that facilitate the Materials Cloud Archive OPTIMADE service to serve the structural data of this Archive entry via an OPTIMADE API. The molecular dynamics trajectories are served as individual structures per time-step.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2024.65</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:vg-ya</dc:identifier> <dc:identifier>mcid:2024.65</dc:identifier> <dc:identifier>oai:materialscloud.org:2158</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>first-principles molecular dynamics</dc:subject> <dc:subject>Li-ion conductors</dc:subject> <dc:subject>solid-state electrolytes</dc:subject> <dc:subject>computational high-throughput screening</dc:subject> <dc:subject>MARVEL/Inc1 </dc:subject> <dc:subject>BIG-MAP</dc:subject> <dc:title>High-throughput computational screening for solid-state Li-ion conductors</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>