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.

Alkali metal ions as some impurity ions in cement raw materials can affect the structure and properties of clinker minerals. The effect of alkali metal ions on the hydration activity and mechanical properties of ternesite was investigated with alkali-doped ternesite synthesized as analytical reagents by isothermal calorimetry, comprehensive thermal analysis, scanning electron microscopy and 29Si solid state nuclear magnetic resonance. The results show that the solid solution of alkali metal ions in the crystal structure of ternesite reduces the crystallinity of formed crystals, forms the crystal defects, effectively improves the early hydration activity of ternesite, and promotes the rapid development of its early mechanical properties. Meanwhile, the addition of alkali metal can change the microstructure and structure of calcium silicate hydrates (C-S-H). Among them, Li2O addition stabilizes the flocculent C-S-H, while Na2O and K2O addition can induce the fibrous growth of C-S-H. The degree of polymerization and mean silicon chain length of C-S-H increase due to the addition of alkali metal.

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 model of developing luminescent materials has transferred from the conventional “trial and error” to “experience- guided experiments”, and further to a novel paradigm of “theoretical prediction and experimental verification”. Efficient theoretical prediction and rapid experimental verification are a key to this transformation. The theoretical prediction methods such as high-throughput calculations and machine learning are becoming more and more mature, and the corresponding experimental methods such as single-particle diagnosis are more efficient, laying a theoretical and experimental foundation for the development of novel luminescent materials. This review briefly summarizes recent research progresses on discovering new rare earth-activated luminescent materials based on single-particle diagnosis and high-throughput computation approaches.

Fullerenes and metallofullerenes have unique structure and novel electronic characteristics, which have great application potential in biomedicine, quantum and information fields. However, how to increase the yields of metallofullerenes is a key technical problem that must be solved for practical application. In order to synthesize metallofullerenes with high yields and selectivity, it is necessary to understand the formation mechanism and develop new synthesis methods. Our research focuses on the comprehensive analysis of the formation mechanism of fullerenes and metallofullerenes in order to find a way to break through the bottleneck of their productivity. On the one hand, the formation process of fullerenes was simulated by density functional theory and molecular dynamics, which directed the optimization of the synthesis conditions of fullerenes. On the other hand, a series of metallofullerenes with specific structures and functions were prepared by accurately controlling the inert gas pressure, arc gas composition and raw material composition of metallofullerenes, and the efficient preparation strategy of metallofullerenes was developed. Finally, we also explored the protection methods after the formation of metallofullerenes, and achieved certain results, laying a solid foundation for the industrialization of metallofullerenes in the future.

The structure of melt is very important for the growth of high-quality large-size functional oxides, which are such materials widely used in military, high-tech manufacturing, and medical treatment. The structures for three typical functional oxide melts, such as oxide alumina, yttrium aluminum garnet, and lithium niobate have been reviewed in this paper. Several coordination units formed by cations and anions exist in each melt, and the coordination number, bond length, spatial structure and the percentage of various coordination units in the melts are obviously different from those in the corresponding crystals. The properties of these structure units are dependent on temperature. The coordination units connected with each other by the nodes. Both the cations and anions in the melts can be acted as nodes to connect the coordination units (polyhedron) in the melts. Phase transition is the kinetic process of melt structure transformation. Therefore, in addition to the melt structure, revealing the melt structure evolution kinetic process will help to understand the growth mechanism of large-sized crystals from a deep level.

Low-dimensional iron-based nanomaterials are representative magnetic nanomaterials used in the field of medicine and healthcare. These nanomaterials have attracted recent attention. This review summarized recent research progress on the synthesis, formation mechanism and biomedical applications of high-performance iron-based nanoparticles, and discussed the controllable synthesis as well as themodulation of interface property. In addition, this review also gave some prospects for biomedical applications of typical two-dimensional iron based nanomaterials and composites.

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.

Machine learning has become an important transformative method to explore novel materials, but the small sample size and high noise of material data bring a great challenge to data-driven research and development. To address the challenge, unsupervised learning was applied to discover perovskite materials with a high dielectric constant. Twenty perovskite materials with a high dielectric constant (i.e., BaHfO3 and BiFeO3) were screened out via iterative clustering. We performed dimensionality reduction analysis and descriptors analysis including elements, crystal structure and tolerance factors to find the underlying trend and the relationship between ABO3 structure and dielectric constant. This method can provide an idea for solving the lack of material data labels, which can be also applied to screen other novel functional materials.

It is ready to cause the interface delamination, macroscopic fracture and spalling failure of thermal barrier coatings (TBCs) due to their complexity of architecture and harsh service environment. In this paper, the failure process of TBCs under three-point bending (3PB) load was monitored in real time via acoustic emission (AE) technology, and the damage failure modes of TBCs were identified based on micro-morphology analysis of AE parameters and K-means cluster. The waveform characteristics of four failure modes were analyzed by fast Fourier transform and wavelet packet transform. The macroscopic fracture or spalling failure signals have no obvious frequency band, while the corresponding frequency components of substrate deformation, surface vertical crack, sliding interface crack and opening interface crack are 62.5?125.0 kHz, 187.5?250.0 kHz, 250.0?312.5 kHz and 375.0?437.5 kHz, respectively. The method of deep machine learning was used to process the in-situ acoustic emission signals. The wavelet energy coefficient was extracted as a characteristic vector of the Back propagation neural network, and the advantages and disadvantages of the model were evaluated by convergence curve, confusion matrix, Receiver operating characteristic curve and F1 value, thus realizing the discrimination of failure modes of TBCs under 3PB test and providing a reference value for failure prediction and life assessment of thermal barrier coatings.

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/?2, 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.

Most data in material science are multi-fidelity data. From the viewpoint of data producer, there is a system error for any quantum method. For machine learning algorithm, as a data consumer, various methods have been designed to maximize the number of knowledges extracted from the multi-fidelity data. In this paper, a quantitative method of noise addition was used to evaluate the influence of different noise types and intensities on some multi-fidelity data learning methods. And the effective scope of the data correction method was verified via iterative noise reduction. The results show that the ways to exploit the multi-fidelity data are crucial. It is necessary to consider comprehensively both the size and the noise level of the datasets. On a variety of datasets constructed with different noise types and intensities, the "Onion" training method that gradually deletes lower fidelity data is better than the "one by one" training method in the direction of noise reduction due to the synergistic effect of different multi-fidelity data. No matter what kind of noise intensity and training method, linear noise has less impact on the final performance of model. However, the data with sampled noise added, which the final testing results are similar to the real multi-fidelity data, were recommended to be adopted in a future research. Also, the complex noise in data is difficult to be corrected by a small amount of true data, thus being more suitable for the iterative noise reduction processing.

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 (TC) of 60 K, which is greater than that for MgB2 (i.e., TC=39 K)

Glass as a material has existed in China for a long time, but the related studies on ancient glass in China started relatively late due to the long-term confusion of name and texture, leading to a lack of research on the weathering and composition of ancient silicate glass. Some previous studies on ancient glass mainly discussed the artistic character and development laws of glass with respect to cultural exchange and chemical analysis from the perspective of dynastic succession. A few work established the related mathematical model and used the intelligent algorithm for qualitative quantification of weathering silicate glass original composition prediction and subclassification method. This paper was to use multiple groups of weathered and unweathered silicate glasses and collect/extract the data on their chemical composition content, ornamentation and color. The relations among the patterns, color, types of glass and surface weathering were analyzed by the Spearman coefficient. The decision tree for a rough classification and neural network to predict the main chemical composition of glass before its weathering was given, and the classification basis of silicate glass was summarized. Besides, the subcategorization at the optimal quantity of categories to conduct subclass classification was established, and a reasonable amount of barium glass and high potassium glass was selected. The results show that the type of glass has an influence on the surface weathering, and there are silicon dioxide, aluminum oxide, lead oxide, barium oxide and phosphorus pentoxide involved in the weathering process. Moreover, the amount of silicon dioxide decreases and lead oxide increases sharply in lead barium glass, while vice versa in high potassium glass after weathering.

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.

The energy storage systems are an important basis for electric vehicles and electronic devices. The existing battery design based on machine learning is able to quickly connect the complex relationship among material microstructure, material properties, and battery macroscopic properties. This review represented the applications and prospects of machine learning in micro-material design and state estimation of batteries. The data sources of machine learning battery design, advantages and disadvantages of algorithms and their application scenarios in the battery field, related innovative work in recent years and their prospects were discussed. This review can provide a reference for machine learning in the macro-/micro-design of energy storage systems.

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.

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.

Recent development of large-scale atomic simulation techniques based on machine learning has brought a great promise in chemistry. These simulations are featured by both high speed and high accuracy. This review outlined recent development on three key aspects of atomic simulation based on machine learning potential, i.e., machine learning models and structure descriptors, generation of global potential energy surface training sets, and automatic training of potential functions based on active learning. It is indicated that the designed structure descriptor and feedforward neural network model are suitable for generating a highly complex global potential energy surface. In addition, the applications of LASP software in material and reaction simulations were also selected to illustrate how ML-based atomic simulation could assist the discovery of novel materials and reactions.

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.

Thermoelectric materials are environmental-friendly energy conversion materials. Their performance optimization is a complex issue of multi-parameter coordination, which becomes a challenge. Although the computational simulation and experimental methods for thermoelectric materials have developed rapidly, the efficiency of searching thermoelectric materials still needs to be further improved. Machine learning has some advantages of low computational cost and high prediction speed, which can shorten the search process and accelerate the corresponding studies on the structure and performance optimization of thermoelectric materials. This review introduced the research progress on machine learning for small sample numerical data (data volume is about 102), large sample numerical data (data volume >104) and image data in thermoelectric materials from the perspective of data types. Moreover, different machine learning algorithm models used for the structure and performance of thermoelectric materials in different data types were discussed. In addition, the future development and application direction were also prospected.

As one of the important simulation methods in computational catalysis, molecular dynamics (MD) simulation plays an important role in understanding the catalytic mechanisms and is critical to the design of efficient and stable catalysts. Classical MD simulation with empirical potentials has a high computational efficiency but a limited accuracy, particularly for systems involving chemical reactions, and the accurate first-principle methods suffer from heavy computational costs and become unaffordable in most cases. The existing emerging machine-learning force field (MLFF) method is proven with affordable computational cost and first-principle-level accuracy. MLFF-assisted MD simulation can offer an effective approach for dynamics simulation in nanoscale catalysis. This review represented the fundamental principle of two main MLFF methods, i.e., the Behler-Parrinello atom-centered neural network method and the embedded-network-based deep potential. The applications of MLFF-assisted dynamic studies related to nano-scale catalysis (i.e., structure reconstruction and reaction processes in catalysis) were described. In addition, some possible future challenges of MLFF methods in dynamics simulation were also given.

Approximately 80% of chemical products used in daily life involve heterogeneous catalytic reactions. How to shorten the catalyst development cycle and efficiently screen catalysts with the superior performance has become a challenge. Among various types of catalysts, two-dimensional materials have attracted recent attention due to their unique structure and electronic properties. It is thus necessary for two-dimensional materials with some specific reaction requirements to clarify the relationship between catalyst structure and function. Descriptors can correlate the structural properties, electronic properties and performance of catalysts, and the structure-activity relationship of catalysts can be revealed through descriptors. The principle of each descriptor?s association with catalyst performance and its mechanism were introduced. Structural descriptors have a better performance in predicting the activity of two-dimensional carbon materials. Compared with the structural descriptors, electronic descriptors can match the parameters of catalytic performance. It is possible to develop the binary descriptors that combine the merits of both structural and electronic descriptors. In the era of big data, the development of descriptors is gradually integrated with emerging methods such as machine learning, and some performance prediction models for multi-dimensional parameters need to be developed. With the progress of artificial intelligence technology, big data-driven performance descriptors will facilitate the development of 2D material catalysts.

With the development of artificial intelligence technology, machine learning atomic interaction potential has become popular to solve a problem regarding the low accuracy of empirical potential. Machine learning atomic interaction potential avoids a low efficiency of conventional fitting method for empirical potential and becomes an emerging tool for material exploration and research. This review represented the characteristics of existing machine learning potential and the applications in phase change, intrinsic properties and interface researches. In addition, the challenge and development trends of machine learning atomic interaction potential were also prospected.

Materials discovery faces a huge and complex high-dimensional search space, from which the fast and effective selection of new materials with target properties is a major challenge in materials development. Machine learning can predict the performance of unexplored materials via establishing the relationship between features and target performance through algorithms based on the existing data. However, there are a relatively few known data for materials, and the machine learning models have a relatively low prediction accuracy, thus making it difficult to achieve an effective guidance for experiments or calculations. To address this problem, active learning was introduced for assistance, and the experimental design step was added to the traditional iterative feedback to select the experiments for target enhancement to supplement and to achieve the optimization of material performance. This review mainly represented recent progress on active learning-assisted materials development from three aspects, i.e., single-objective optimization, multi-objective optimization, and curve optimization.

Crystal structure prediction is a powerful theoretical simulation tool, which can determine the crystal structure of materials with the given information of chemical composition. However, its application is severely limited due to the highly computational cost. In recent years, the state-of-art machine learning methods reveal a promising prospect in accelerating the conventional scientific computing, thus introducing the methods into the crystal structure prediction. This review briefly introduced recent progress on the application of machine learning for the crystal structure prediction. Two aspects were discussed, i.e., accelerating the energy evaluation and enhancing the potential energy surface sampling. In addition, some insights into the future development in this aspect were also suggested.