An effective method to screen sodium-based layered materials for sodium ion batteries

Zhang, Xu; Zhang, Zihe; Yao, Sai; Chen, An; Zhao, Xudong; Zhou, Zhen

doi:10.1038/s41524-018-0070-2

Cited by 93 publications

(100 citation statements)

References 42 publications

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“…To this end, we checked the performance of the models on the Naset. This data set is taken from the work of Zhang et al 57 and contains voltage information of 32 Na-battery electrode materials with voltages in the range of 0.7 -6.5 V. This set of materials was obtained by screening the entire Materials Project database for Na-based layered materials and then performing DFT calculations on the selected set of materials suitable as battery electrodes. The Na-set differs from the original T-and H-set as it contains the voltage for Na-based electrode materials (for which our model is not trained).…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Joshi

Eickholt

et al. 2019

ACS Appl. Mater. Interfaces

138

106

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Machine learning (ML) techniques have rapidly found applications in many domains of materials chemistry and physics where large data sets are available. Aiming to accelerate the discovery of materials for battery applications, in this work, we develop a tool (http://se.cmich.edu/batteries) based on ML models to predict voltages of electrode materials for metal-ion batteries. To this end, we use deep neural network, support vector machine, and kernel ridge regression as ML algorithms in combination with data taken from the Materials Project Database, as well as feature vectors from properties of chemical compounds and elemental properties of their constituents. We show that our ML models have predictive capabilities for different reference test sets and, as an example, we utilize them to generate a voltage profile diagram and compare it to density functional theory calculations. In addition, using our models, we propose nearly 5,000 candidate electrode materials for Na-and K-ion batteries. We also make arXiv:1903.06813v2 [cond-mat.mtrl-sci] 9 May 2019 available a web-accessible tool that, within a minute, can be used to estimate the voltage of any bulk electrode material for a number of metal-ions. These results show that ML is a promising alternative for computationally demanding calculations as a first screening tool of novel materials for battery applications.

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Section: Resultsmentioning

confidence: 99%

“…Voltage profile diagram obtained from different ML models and DFT for Na x Co 2 SbO 6 . The DFT values are taken from the work of Zhang et al57 …”

mentioning

confidence: 99%

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Joshi

Eickholt

et al. 2019

ACS Appl. Mater. Interfaces

138

106

View full text Add to dashboard Cite

show abstract

“…Recently, material explorations using computational screening have assisted in the identification of promising candidates using high-throughput material databases based on first principles calculations. [24][25][26][27] Promising materials were identified for coatings at the interface of solid electrolytes, 28,29 cathode materials, [30][31][32][33][34] anode materials, 35,36 solid-state electrolyte, 37 Mg battery electrolytes, 38 photo-catalysts, 39 and fuel cell electrodes. 40 Our earlier works also presented novel and promising anode materials for SIBs using high-throughput screening.…”

Section: Introductionmentioning

confidence: 99%

Computational screening of anode materials for potassium‐ion batteries

Kim

et al. 2019

Int J Energy Res

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Summary Potassium ion batteries (PIBs) are promising alternatives to Li‐ion batteries due to the abundance of potassium. However, the development of PIBs is in its early stages, and only a few anode materials have been reported. Herein, we explored anode materials for PIBs using high‐throughput computational screening. The alloying and conversion reactions of prospective anode materials were investigated using the Materials Project database. Grand potential diagrams were obtained to examine the reaction potential and theoretical capacity of the anode materials. Calculation results indicated that P, As, Si, and Sb anodes exhibited high theoretical capacities. In addition, the screening results revealed that phosphides generally exhibit a lower reaction potential compared with those of sulfides and oxides. A total of 18 binary compounds that exhibited a high theoretical capacity and a low reaction potential (greater than 450 mAh/g and 0.7 V) were identified as promising anode materials for PIBs. In particular, ZnP2, CuP2, SiP, NiP3, CoP3, V3S4, Nb3S5, Bi2O3, and Ga2O3 exhibited high theoretical capacities.

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“…The singular example is the extension of the topology scaling method to screen Na-containing materials for battery applications. 21 Therein, the layers are ionically bound by the electropositive Na cation. The procedure involves selective removal of Na atoms from the structure and analysis of the pseudo-structure with the topology scaling method.…”

Section: Introductionmentioning

confidence: 99%

Ionic vs. van der Waals layered materials: identification and comparison of elastic anisotropy

et al. 2018

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In this work, we expand the set of known layered compounds to include ionic layered materials, which are well known for superconducting, thermoelectric, and battery applications. Focusing on known ternary compounds from the ICSD, we screen for ionic layered structures by expanding upon our previously developed algorithm for identification of van der Waals (vdW) layered structures, thus identifying over 1,500 ionic layered compounds. Since vdW layered structures can be chemically mutated to form ionic layered structures, we have developed a methodology to structurally link binary vdW to ternary ionic layered materials. We perform an in-depth analysis of similarities and differences between these two classes of layered systems and assess the interplay between layer geometry and bond strength with a study of the elastic anisotropy. We observe a rich variety of anisotropic behavior in which the layering direction alone is not a simple predictor of elastic anisotropy. Our results enable discovery of new layered materials through intercalation or deintercalation of vdW or ionic layered systems, respectively, as well as lay the groundwork for studies of anisotropic transport phenomena such as sound propagation or lattice thermal conductivity. arXiv:1805.10489v1 [cond-mat.mtrl-sci]

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An effective method to screen sodium-based layered materials for sodium ion batteries

Cited by 93 publications

References 42 publications

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Computational screening of anode materials for potassium‐ion batteries

Ionic vs. van der Waals layered materials: identification and comparison of elastic anisotropy

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