2026-05-27T08:08:17Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:nf76v-1eh14 2026-04-21T15:59:44Z openaire_data community-mcarchive

Thakur, Tushar Singh Thakur, Tushar Singh Marzari, Nicola 2026-04-21 <p>We present a high-throughput computational screening for fast lithium-ion conductors aimed at identifying candidate materials for application in all-solid-state electrolytes. Beginning with more than 30,000 experimentally reported Li-containing structures drawn from the Inorganic Crystal Structure Database, the Materials Platform for Data Science (Pauling file), and the Crystallography Open Database, we apply a series of automated structural and compositional filters to obtain 1500 unique crystal structures suitable for electronic-structure calculations which yields nearly 1,000 electronic insulators. We then estimate Li-ion diffusivities for these insulating candidates using molecular dynamics simulations at multiple temperatures. To make simulations computationally feasible at this scale while preserving near first-principles fidelity, we employ a foundational machine-learned interatomic potential which is carefully fine-tuned on relevant Li-chemistries. We discuss the details of the fine-tuning strategies and data-consistency considerations required to obtain a very accurate and robust model. From the MD results, we identify three particularly promising novel oxide candidates for room-temperature solid-state electrolytes, including LiSn<sub>2</sub>(AsO<sub>4</sub>)<sub>3</sub>, LiIn(IO<sub>3</sub>)<sub>4</sub>, and LiB<sub>6</sub>S<sub>4</sub>(Cl<sub>3</sub>O<sub>4</sub>)<sub>2</sub>, which all exhibit ionic conductivity greater than 1 mS/cm at room temperature with diffusion barrier between 0.20 and 0.25 eV. We provide the full screening protocol as well as a prioritised list of materials for experimental follow-up, demonstrating the value of provenance-aware, ML-driven simulations in accelerating solid-state electrolyte discovery. </p> application/octet-stream chemical/x-xyz application/x-xz text/plain https://doi.org/10.24435/materialscloud:1c-13 oai:materialscloud.org:nf76v-1eh14 mcid:2026.83 eng Materials Cloud https://renkulab.io/p/aiida/materials-cloud-archive/sessions/01JZAQ1T34GEE1S98BV1300FXY/start?archive_url=https://archive.materialscloud.org/api/records/nf76v-1eh14/files/structures.aiida/content https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:xa-e8 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode first principles molecular dynamics Li-ion conductors solid-state electrolytes high-throughput DFT High-throughput screening for solid-state Li-ion conductors combining machine learning and first-principles calculations info:eu-repo/semantics/other