Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries.

ABSTRACT: In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials. For this purpose, a density functional theory modeling approach is employed to predict basic quantum mechanical quantities such as redox potentials, and electronic properties such as electron affinity, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), for a selected set of organic materials. Both the electronic properties and structural information, such as the numbers of oxygen atoms, lithium atoms, boron atoms, carbon atoms, hydrogen atoms, and aromatic rings, are considered as input variables for the machine learning-based prediction of redox potentials. The large-set of inp

SUBMITTER: Allam O 

PROVIDER: S-EPMC9090775 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature