Li metal is regarded as an ideal anode for the next-generation secondary batteries. However, the growth of Li-dendrite results in a low coulomb efficiency, thus restricting the commercial application of Li secondary batteries. The mechanism of Li deposition and growth in an atomic scale is still unclear, and there are different opinions about the origin of Li dendrite. Recent work has dealt with the application of machine learning in computational materials science. This review represented the applications of machine learning potential for the study of Li metal anode.

Solid-sate lithium battery (SSB) is considered as one of the most promising next-generation batteries due to its high energy density. The emergence of machine-learning (ML) techniques affords a possibility for the study of solid-state electrolytes (SSEs). ML is able to promote a deep application of theoretical simulations in SSB and build a high-accuracy and multi-scale simulation paradigm. Besides, ML can establish a quantitative structure-function relation of SSEs and achieve a high-throughput screening of advanced SSEs. In addition, ML-assisted experiments can synthesize advanced SSEs with a high efficiency and deliver a comprehensive understanding of working mechanism in SSBs with various characterizations such as synchrotron imaging. Therefore, the introduction of ML and its combination with the existing theoretical and experimental methods can promote the study of SSEs and the practical application of SSBs definitely.

Surface energy is one of the most important physical and chemical properties for crystals, which has a significant impact on surface catalysis, surface adsorption, epitaxial growth, nucleation, and dendrite growth. Rapid calculation and prediction of crystal surface energies can favor accelerating the design and optimization of catalysis materials, battery materials, and alloys. In this paper, a data-driven machine learning algorithm was proposed with a crystal graph convolutional neural network framework for the prediction of metal surface energy from the crystal structure. Using a physics-based surface representation that couples the surface dimensions to the atomic and bonding features of the crystal, we obtained an MAE value of less than 0.002 eV/Ų, which surpasses other math-based surface models. Compared with the first-principles calculation, the computation time is reduced by approxiamtely 5 orders of magnitude. In addition, we discussed the main challenges and solutions towards the surface energy prediction of more complicated systems such as Silicates. It is expected that this work could be a paradigm for the surface energy prediction with machine learning.

Organic-inorganic hybrid perovskites have promising applications in solar cells and other optoelectronic devices due to their superior physical and chemical properties. A band gap is a key physical characterization that is related to the efficiency of solar energy conversion. We performed machine learning for the band gap of hybrid perovskites, and investigated the influence of structural features based on the Voronoi method on the model accuracy. The results show that compared to machine learning with only element features as an input, the accuracy of the band-gap models can be improved when the Voronoi structural feature is included in all the three methods of symbolic regression (VS-SISSO), artificial neural network (ANN) and random forest (RF). In particular, the Voronoi structural feature is of vital importance in the VS-SISSO model for its nature of being simple explicit expressions. The models from the three machine learning methods have comparable prediction accuracies, and the VS-SISSO model has better transparency and interpretability. According to the feature importance analysis, the Voronoi structural feature is the most important among all the input features.

Applications, evaluations and funding of National Natural Science Foundation of China programs on inorganic nonmetallic materials in 2022 were summarized and statistically analyzed. Management of National Natural Science Foundation of China programs was summarized. The specific measures of inorganic nonmetallic materials on scientific fund reform, talents and academic teams, academic exchange and seminars, organization of major projects were introduced detailed.

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.

Bioprocessing-inspired fabrication technology of materials is inspired by a subtle structure formation process of natural materials, which is a developing technology for materials synthesis and preparation. This review introduced recent development on the bioprocessing-inspired fabrication technology of materials around the bio-processing and the relationship between bio-processing and bio-structure. The existing related research work was represented from the aspects of bio mineralization-inspired synthesis and preparation, photosynthesis-inspired synthesis, and synthesis and preparation of a combination of photosynthesis and bio mineralization. In addition, the research objectives of bioprocessing-inspired fabrication technology of materials were also summarized, and the development trend was prospected as well.

The development and application of high performance potassium ion batteries (PIBs) is a major demand for China's strategic emerging industries, and it is also a new system and direction for the development of energy storage secondary batteries. However, the current research on PIBs is still in its initial stage, and still faces the challenges of slow diffusion kinetics, unclear transport mechanism, rapid capacity decay and difficulty in revealing the intrinsic decay mechanism. This paper summarizes the latest research results of the National Natural Science Foundation of China (NSFC) project "Surface/Interface Tuning and In Situ Interaction Mechanism of Hierarchical Mesoporous Nanowire Cathodes for Potassium Ion Battery", systematically describes the key scientific problems and technical bottlenecks in PIB research, and points out the efficient strategies to solve these problems and bottlenecks.

Searching for high-temperature ambient-pressure superconductors is a challenge in materials science. Machine learning has a promising application in materials discovery. A data-driven approach that overcomes low-data limitations by computationally inexpensive descriptors based on the Bardeen-Cooper-Schrieffer (BCS) theory and semi-supervised learning was proposed. The accuracy of the classification mode is 72%. This approach can screen over 10 000 binary and ternary BCS compounds in the Material Project database, thus identifying some promising superconductors at ambient pressure. The compounds in B-C and B-C-N systems have a maximum superconducting critical temperature (T_C) of 60 K, which is greater than that for MgB₂ (i.e., T_C=39 K)

The methods of machine learning based on the data science can deal with the corresponding studies in different disciplines based on the data accumulated in theory and experiments. Machine learning promotes the development of data-intensive scientific discoveries, thus making it a "fourth paradigm" that leads to the related scientific research after "theory, calculation, and experimentation". Among different materials, perovskite material has some unique advantages of rich composition, adjustable band gap, and broad development space, but this material does not reach the practical standards such as environmental friendliness in applications. Therefore, the exploration of perovskite material and its applications based on machine learning can accelerate the discovery of novel perovskite material, and explore the relationship between the physical and chemical characteristics of perovskite material, therefore providing a guidance for the development of environmentally friendly high-performance perovskite devices. This review represented the research process of machine learning for perovskite material, summarized some research work on machine learning in perovskite material properties and device exploration, and discussed the existing difficulties and challenges. In addition, the future development direction and trend were also prospected.