Accelerating the Search for New Solid Electrolytes: Exploring Vast Chemical Space with Machine Learning-Enabled Computational Calculations.
ACS Appl Mater Interfaces
; 2023 Nov 04.
Article
en En
| MEDLINE
| ID: mdl-37924286
ABSTRACT
Discovering new solid electrolytes (SEs) is essential to achieving higher safety and better energy density for all-solid-state lithium batteries. In this work, we report machine learning (ML)-assisted high-throughput virtual screening (HTVS) results to identify new SE materials. This approach expands the chemical space to explore by substituting elements of prototype structures and accelerates an evaluation of properties by applying various ML models. The screening results in a few candidate materials, which are validated by density functional theory calculations and ab initio molecular dynamics simulations. The shortlisted oxysulfide materials satisfy key properties to be successful SEs. The advanced screening method presented in this work will accelerate the discovery of energy materials for related applications.
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1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
ACS Appl Mater Interfaces
Asunto de la revista:
BIOTECNOLOGIA
/
ENGENHARIA BIOMEDICA
Año:
2023
Tipo del documento:
Article