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Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety. However, a lack of reliable cathode materials has largely pledged their applications. Herein, we developed a machine learning (ML) based approach to predict cathodes with high capacity (>150 mAh/g) and high voltage (>0.5V). We screened over ~130,000 inorganic materials from the Materials Project database and applied the crystal graph convolutional neural network (CGCNN) based ML approach with data from the AFLOW database. The combination of these two could not only screen cathode materials that match well with the experimental data but also predict new promising candidates for further experimental validations. We hope this study could spur further interests in ML-based advanced theoretical tools for battery materials discovery.
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending and general
Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials prope
Bismuth has recently attracted interest in connection with Na-ion battery anodes due to its high volumetric capacity. It reacts with Na to form Na$_3$Bi which is a prototypical Dirac semimetal with a nontrivial electronic structure. Density-functiona
In this work, we develop a combined convolutional neural networks (CNNs) and finite element method (FEM) to examine the effective thermal properties of composite phase change materials (CPCMs) consisting of paraffin and copper foam. In this approach,
Geometric information such as the space groups and crystal systems plays an important role in the properties of crystal materials. Prediction of crystal system and space group thus has wide applications in crystal material property estimation and str