Faculty Opinions recommendation of A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning.

2019 ◽  
Author(s):  
Pengyu Ren ◽  
Chengwen Liu
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Force Field ◽  
Density Functional ◽  
Quantum Level ◽  
Functional Theory ◽  
Parameterization Method ◽  
Force Field Parameterization ◽  
Molecular Force ◽  
Molecular Force Field

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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