Density Functional Theory and Machine Learning Based Analyses for Improved Surface Stability of a BaTiO 3 ‐Coated LiCoO 2 Positive Electrode Material

2021 ◽  
Author(s):  
Kunihiro Ishida ◽  
Naoto Tanibata ◽  
Hayami Takeda ◽  
Masanobu Nakayama ◽  
Takashi Teranishi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Electrode Material ◽  
Density Functional ◽  
Positive Electrode ◽  
Functional Theory ◽  
Surface Stability ◽  
Positive Electrode Material

Predicting the stability of ternary intermetallics with density functional theory and machine learning

2018 ◽  
Vol 148 (24) ◽  
pp. 241728 ◽  
Author(s):  
Jonathan Schmidt ◽  
Liming Chen ◽  
Silvana Botti ◽  
Miguel A. L. Marques
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Density Functional ◽  
Functional Theory ◽  
The Stability

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

RSC Advances ◽  
2018 ◽  
Vol 8 (69) ◽  
pp. 39414-39420 ◽  
Author(s):  
Omar Allam ◽  
Byung Woo Cho ◽  
Ki Chul Kim ◽  
Seung Soon Jang
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
High Throughput Screening ◽  
Density Functional ◽  
Electrode Materials ◽  
Screening Method ◽  
Li Ion Batteries ◽  
Functional Theory ◽  
Learning Framework ◽  
Li Ion

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.

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