Improved performance of graphene doped with pyridinic N for Li-ion battery: a density functional theory model

2013 ◽  
Vol 15 (31) ◽  
pp. 12982 ◽  
Author(s):  
Xiang-kai Kong ◽  
Qian-wang Chen
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Theory Model ◽  
Li Ion Battery ◽  
Functional Theory ◽  
Li Ion ◽  
Improved Performance ◽  
Pyridinic N

scholarly journals A density functional theory study of the carbon-coating effects on lithium iron borate battery electrodes

2017 ◽  
Vol 19 (3) ◽  
pp. 2087-2094 ◽  
Author(s):  
Simon Loftager ◽  
Juan María García-Lastra ◽  
Tejs Vegge
Keyword(s):  
Density Functional Theory ◽  
Cathode Material ◽  
Carbon Coating ◽  
Density Functional ◽  
Li Ion Battery ◽  
Density Functional Theory Study ◽  
Iron Borate ◽  
Functional Theory ◽  
Carbon Coatings ◽  
Li Ion

Density functional theory modelling shows that carbon coatings on a LiFeBO3 cathode material does not impede the Li transport in a Li-ion battery.

Reciprocal Polarizable Embedding with a Transferable H2O Potential Function II: Application to (H2O)n Clusters & Liquid Water

2019 ◽  
Author(s):  
Asmus Ougaard Dohn ◽  
Elvar Jónsson ◽  
Hannes Jonsson
Keyword(s):  
Density Functional Theory ◽  
Liquid Water ◽  
Density Functional ◽  
Water Clusters ◽  
Theory Model ◽  
Coupling Scheme ◽  
Functional Theory ◽  
Total Polarization ◽  
Electrostatic Embedding ◽  
Polarizable Embedding

The manuscript analyzes the accuracy of our recently developed reciprocal polarizable embedding scheme, where a density functional theory model of the QM region is coupled to a dipole- and quadrupole polarizable water potential of the MM region. We present calculations of water clusters and liquid water where we analyze the energy, atomic forces and total polarization to demonstrate that artifacts in energy and polarization introduced by the QM/MM coupling are small and well-behaved. Furthermore, our methodology improves the consistency of the structure of optimized water hexamer geometries when compared to results obtained with models that neglect polarization. Additionally, the manuscript provides evidence that our coupling scheme eliminates artifacts in the structure of liquid water obtained with simpler electrostatic embedding models.

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.

Metallic FeSe monolayer as an anode material for Li and non-Li ion batteries: a DFT study

2020 ◽  
Vol 22 (16) ◽  
pp. 8902-8912 ◽  
Author(s):  
Xiaodong Lv ◽  
Fengyu Li ◽  
Jian Gong ◽  
Jinxing Gu ◽  
Shiru Lin ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Electrochemical Performance ◽  
Lithium Ion Batteries ◽  
Anode Material ◽  
Density Functional ◽  
Lithium Ion ◽  
Li Ion Batteries ◽  
Functional Theory ◽  
Li Ion ◽  
Density Functional Theory Computations

By means of density functional theory computations, we explored the electrochemical performance of an FeSe monolayer as an anode material for lithium and non-lithium ion batteries (LIBs and NLIBs).

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