The 0.93(Na_0.5Bi_0.5)_1-xSm_xTiO₃-0.07BaTiO₃ multifunctional ceramics were prepared by solid-phase reaction method. The phase structure, microstructure, electrical and photoluminescent properties were systematically studied. With increasing x, the ceramics undergoes the phase transition from rhombohedral to tetragonal with some rhombohedral distortion, along with a reduced grain size and increased relative density. On the other hand, the Sm³⁺ doping enhances the electric-field driven reversible phase transition and domain size, and reduces the domain walls, thereby contributing to improved piezoelectricity and decreased depolarization temperature (T_d) from 91 ℃ to 40 ℃. Excellent piezoelectric properties of d₃₃ = 213 pC/N and k_p = 29.9% are achieved in the x = 0.010 ceramic. Under excitation (407 nm), the Sm³⁺-doped ceramic exhibits bright reddish-orange fluorescence at 564, 599, 646 nm and 710 nm. A polarization-induced enhancement of photoluminescence is obtained in BNBT-xSm ceramics with an improved relative intensity of emission band at 646 nm. These results indicate that Sm³⁺-doped BNBT ceramics show great potential in electro-optic integration and coupling device applications.

Lithium dendrite growth due to uneven electrodeposition may penetrate the separator and solid electrolyte, causing inner short circuit and potential thermal runaway. Despite great electrochemical phase-field simulation efforts devoted to exploring the dendrite growth mechanism under the temperature field, no unified picture has emerged. For example, it remains open how to understand the promotion, inhibition, and dual effects of increased temperature on dendrite growth when using different electrolyte types. Here, by comprehensively considering the temperature-dependent Li⁺ diffusion coefficient, electrochemical reaction coefficient, and initial temperature distribution in phase-field model, we propose that the activation–energy ratio, defined as the ratio of electrochemical reaction activation energy to electrolyte Li⁺ diffusion activation energy, can be used to quantify the effect of temperature on dendrite morphology. Specifically, we establish a mechanism diagram correlating the activation–energy ratio, uniform initial temperature, and maximum dendrite height, which unifies the seemingly contradictory simulation results. Furthermore, results based on nonuniform initial temperature distribution indicate that a positive temperature gradient along the discharging current facilitates uniform Li⁺ deposition and local hotspot should be avoided. These findings provide valuable insights into the temperature-dependent Li dendrite growth and contribute to the practical application of Li metal batteries.

Propylene carbonate (PC)-based electrolytes have exhibited significant advantages in boosting the low-temperature discharging of graphite-based Li-ion batteries. However, it is still unclear whether they can improve the charging property and suppress lithium plating. Studying this topic is challenging due to the problem of electrochemical compatibility. To overcome this issue, we introduced graphite with phase defects. The results show that the pouch-type full batteries using PC-based electrolyte exhibit steady performance over 500 cycles and can be reversibly charged over 30 times at −20 °C with an average Coulombic efficiency of 99.95%, while the corresponding value for the conventional ethylene carbonate (EC)-based electrolyte sample is only 31.20%. This indicates that the use of PC-based electrolyte significantly suppresses lithium plating during low-temperature charging. We further demonstrate that the improved performance is mainly attributed to the unique solvation structure, where more ${\mathrm{P}\mathrm{F}}_6^{-}$ anions participate in solvation, leading to the formation of a stable F-rich solid state electrolyte interface on the graphite surface and a lower reduction tendency of Li⁺ ions. This work inspires new ideas for the design of PC-based electrolytes for low-temperature charging and fast-charging batteries.

Inorganic–polymer composite solid electrolytes (IPCSEs) obtained by filling the polymer matrix with inorganic materials usually have higher ionic conductivity compared with individual phases. This important increase in ionic conductivity is explained in terms of the new percolation paths formed by the highly conductive interface between inorganic filler and polymer. The conduction in such systems can be investigated using the effective medium theory (EMT) and random resistance model (RRM). EMT can be used to analyze the effect of filler size on the ionic conductivity of disordered IPCSEs, while RRM can describe the composites with inorganic fillers of various shapes (nano-particles, nano-wires, nano-sheets, and nano-networks) in ordered or disordered arrangement. Herein, we present software evaluating the ionic conductivity in IPCSEs by combining EMT and RRM. The approach is illustrated by considering the size, shapes, and arrangements of inorganic fillers. The ionic conductivities of different types of IPCSEs are predicted theoretically and found in good agreement with the experimental values. The software can be used as an auxiliary tool to design composite electrolytes.

Distortion manipulation emerges as an efficient approach to obtain desired perovskite phases for various applications. In part Ⅰ of this study, we propose a paradigm to quantify the structural distortion manipulation, which enables us to obtain desired perovskite phases by translating relevant materials research into a single mathematical question. As part Ⅱ of this continuous study, we construct normalized structures by introducing all possible couplings of dominant distortions into a cubic supercell and then compare them with variously shaped primitive/conventional cells known in the database. The structure comparison demonstrates that distortions are the only cause for phase and property variations. This confirms that our proposed distortion parameters can be directly used to construct phases, providing theoretical support for the paradigm in Part Ⅰ. Given the limited number of distortion types, we identify that the positional relations involved in distortion arrangements and couplings are the keys to describe numerous phases. Furtherly, a three-step workflow is proposed with core contents related to the positional relation, distortion hierarchy, and distortion-component-generation ordering in spatial dimension, respectively. The definition basis and value changes of distortion/model parameters in this workflow illustration provide guidelines about how to reveal the logic behind the perovskite phase evolution.

Slight distortions can cause dramatic changes in the properties of crystalline perovskite materials and their derivatives. Due to the numerous types of distortions and unclarified distortion-structure relations, a quantitative distortion manipulation for the desired crystalline phase of perovskite materials suitable for various application remains challenging. Here, by establishing parameter sets to systematically describe the types, magnitudes and positional relations involved in distortions, we are able to interpret the structural regulations and manipulation strategies in 7 reported crystal systems. Through the construction of distortion-phase-property functional curves, we further propose a paradigm to quantify the structural distortion manipulation for desired perovskite phases. Using the example of perovskite-like tungsten oxides, we successfully quantify their volume shrinkage and symmetry increase during lithiation. This work verifies that the complicated research and development of perovskite materials can be simplified into a mathematical problem solving process, which will inspire researchers with different backgrounds to participate, especially mathematicians and computer scientists.

Generative Artificial Intelligence (GAI) is attracting the increasing attention of materials community for its excellent capability of generating required contents. With the introduction of Prompt paradigm and reinforcement learning from human feedback (RLHF), GAI shifts from the task-specific to general pattern gradually, enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships. Here, we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology. The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected. Taking ChatGPT as an example, we explore the potential applications of general GAI in generating multiple materials content, solving differential equation as well as querying materials FAQs. Furthermore, we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions. This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

Data-driven machine learning is widely used in materials property prediction and structure-activity relationship research due to its accurate and efficient predictive ability. Data determines the upper limit of machine learning. However, materials data often have various quality and quantity problems (i.e., multiple sources, large noise, small samples, and high dimensionality), affecting the application of machine learning in the materials field. In this paper, by analyzing the data quality and quantity problems and their related governance work, we find that data quality and data quantity jointly determine this problem. Following this, a data quality and quantity governance framework embedded by materials domain knowledge in the whole process of materials machine learning is proposed. We define twelve dimensions to analyze the connotation of materials data quality and quantity. A life cycle model of data quality and quantity governance is constructed to ensure that data quality and quantity governance activities are carried out in an orderly manner. To manage data quality and quantity accurately and comprehensively, a series of corresponding governance processing models are established from domain knowledge and data-driven aspects, which provides technical support for the specific implementation of the life cycle model. This framework realizes the overall evaluation and improvement of materials data quality and quantity, providing theoretical guidance and candidate solutions for high-quality and appropriate-quantity data acquisition and accelerating the in-depth application of machine learning in materials research and development.

Data-driven Machine Learning (ML) has been widely used in materials performance optimization and novel materials design due to its ability to quickly fit potential data patterns and achieve accurate prediction. However, the results of data-driven ML are often inconsistent with the materials basic theory or principle, which results mainly from the lack of the guidance of materials domain knowledge, e.g., the correlation among descriptors and the driving mechanism associated with the properties. Herein, by analyzing the characteristics of materials data and the modeling principle of data-driven ML methods, we clarify the three main contradictions occurring to the application of ML in materials science, i.e., the contradictions between high dimension and small sample, complexity and accuracy of models, learning results and domain knowledge. Following this, we propose the ML method embedded with materials domain knowledge to reconcile these three contradictions. Further, surrounding the whole ML process including target definition, data collection and preprocessing, feature engineering, model construction and application, we explore some key techniques to realize domain knowledge embedding by summarizing the related basic and exploratory efforts. Finally, opportunities and challenges facing the ML method embedded with domain knowledge are also discussed.

Lithium-rich antiperovskites are promising solid-state electrolytes for all-solid-state lithium-ion batteries because of their high structural tolerance and good formability. However, the experimentally reported proton-free Li₃OCl is plagued by its inferior interfacial compatibility and harsh synthesis conditions. In contrast, Li₂OHCl is a thermodynamically favored phases and is easier to achieve than Li₃OCl. Due to the proton inside this material, it exhibits interesting lithium diffusion mechanisms. Herein, we present a systematic investigation of the ionic transport, phase stability, and electrochemical-chemical stability of Li₂OHCl using first-principles calculations. Our results indicate that Li₂OHCl is thermodynamically metastable and is an electronic insulator. The wide electrochemical stability window and high chemical stability of Li₂OHCl against various electrodes are confirmed. The charged defects are the dominant conduction mechanism for Li-transport, with a low energy barrier of ~0.50 eV. The Li-ion conductivity estimated by ab initio molecular dynamics simulations is about 1.3 × 10⁻⁴ S cm⁻¹ at room temperature. This work identifies the origin of the high interfacial stability and ionic conductivity of Li₂OHCl, which can further lead to the design of such as a cathode coating. Moreover, all computational methods for calculating the properties of Li₂OHCl are general and can guide the design of high-performance solid-state electrolytes.