scholarly journals Identification of the Selective Sites for Electrochemical Reduction of CO to C2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning

2018 ◽  
Vol 3 (12) ◽  
pp. 2983-2988 ◽  
Author(s):  
Yufeng Huang ◽  
Yalu Chen ◽  
Tao Cheng ◽  
Lin-Wang Wang ◽  
William A. Goddard
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Electrochemical Reduction ◽  
Copper Nanoparticles ◽  
Density Functional ◽  
Force Fields ◽  
Reactive Force ◽  
Functional Theory ◽  
Reactive Force Fields

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.

Enhancing Gas Solubility in Nanopores: A Combined Study Using Classical Density Functional Theory and Machine Learning

Langmuir ◽  
2020 ◽  
Vol 36 (29) ◽  
pp. 8527-8536 ◽  
Author(s):  
Chongzhi Qiao ◽  
Xiaochen Yu ◽  
Xianyu Song ◽  
Teng Zhao ◽  
Xiaofei Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Density Functional ◽  
Gas Solubility ◽  
Functional Theory
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