Machine learning and density functional theory simulation of the electronic structural properties for novel quaternary semiconductors

Recent Advances in High-Entropy Layered Oxide Cathode Materials for Alkali Metal-Ion Batteries

Since the electrochemical de/intercalation behavior is first detected in 1980, layered oxides have become the most promising cathode material for alkali metal-ion batteries (Li+/Na+/K+; AMIBs) owing to their facile synthesis and excellent theoretical capacities. However, the inherent drawbacks of unstable structural evolution and sluggish diffusion kinetics deteriorate their electrochemical performance, limiting further large-scale applications. To solve these issues, the novel and promising strategy of high entropy has been widely applied to layered oxide cathodes for AMIBs in recent years. Through multielement synergy and entropy stabilization effects, high-entropy layered oxides (HELOs) can achieve adjustable activity and enhanced stability. Herein, the basic concepts, design principles, and synthesis methods of HELO cathodes are introduced systematically. Notably, it explores in detail the improvements of the high-entropy strategy on the limitations of layered oxides, highlighting the latest advances in high-entropy layered cathode materials in the field of AMIBs. In addition, it introduces advanced characterization and theoretical calculations for HELOs and proposes potential future research directions and optimization strategies, providing inspiration for researchers to develop advanced HELO cathode materials in the areas of energy storage and conversion.

High-Entropy Oxides for Rechargeable Batteries

High-entropy oxides (HEOs) have garnered significant attention within the realm of rechargeable batteries owing to their distinctive advantages, which encompass diverse structural attributes, customizable compositions, entropy-driven stabilization effects, and remarkable superionic conductivity. Despite the brilliance of HEOs in energy conversion and storage applications, there is still lacking a comprehensive review for both entry-level and experienced researchers, which succinctly encapsulates the present status and the challenges inherent to HEOs, spanning structural features, intrinsic properties, prevalent synthetic methodologies, and diversified applications in rechargeable batteries. Within this review, the endeavor is to distill the structural characteristics, ionic conductivity, and entropy stabilization effects, explore the practical applications of HEOs in the realm of rechargeable batteries (lithium-ion, sodium-ion, and lithium-sulfur batteries), including anode and cathode materials, electrolytes, and electrocatalysts. The review seeks to furnish an overview of the evolving landscape of HEOs-based cell component materials, shedding light on the progress made and the hurdles encountered, as well as serving as the guidance for HEOs compositions design and optimization strategy to enhance the reversible structural stability, electrical properties, and electrochemical performance of rechargeable batteries in the realm of energy storage and conversion.

Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies

The present paper is primarily focused on predicting the band gaps of nitride perovskites from machine learning (ML) models. The ML models have been framed from the feature descriptors and band gap values of 1563 inorganic nitride perovskites having formation energies <-0.026 eV and band gaps ranging from ∼1.0 to 3.1 eV. Four supervised ML models such as multi-layer perceptron (MLP), gradient boosted decision tree (GBDT), support vector regression (SVR) and random forest regression (RFR) have been considered to predict the band gaps of the said systems. The accuracy of each model has been tested from mean absolute error, root-mean-square error and determination coefficient R2 values. The bivariate plots between the predicted and input band gaps of the compounds for both the training and test datasets have also been estimated. Additionally, two ABN3-type nitride perovskites CeBN3 (B = Mo, W) have been selected and their electronic band structures and optoelectronic properties have been studied from density functional theory (DFT) calculations. The band gap values of the said compounds have been estimated from DFT calculations at PBE, HSE06, G0W0@PBE, G0W0@HSE06 level of theories. The present study will be helpful in exploring the ML models in predicting the band gaps of nitride perovskites which in turn may bear potential applications in photovoltaic cells and optical luminescent devices.