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Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach’s effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys.

In this study, we report a novel high-Mn Fe–21Mn–6Al–4Si–1C (wt.%) duplex lightweight steel with concurrent chemical ordering and twinning-induced plasticity effects. This steel is capable of achieving an exceptional combination of strength and ductility following either cold-rolling and annealing at 1000 °C or subsequent short-term aging at 550 °C. In its as-annealed state, this steel primarily consists of γ-austenite and α-ferrite, with α-ferrite appearing as dispersed particles within fully recrystallized γ grains. Furthermore, L′12- and D03-type ordered nanodomains exist within these two phases, respectively. The aging treatment negligibly affects the size and volume fraction of both the γ-austenite and α-ferrite, yet it enhances the degree of ordering as well as the size and volume fraction of their ordered nanodomains, leading to a rise in yield strength from ∼800 to ∼1062 MPa and a decline in total elongation from ∼60.4% to ∼44.4%. The high yield strength of this steel originates from multiple strengthening mechanisms involving dislocation interactions with solute atoms, ordered nanodomains, γ grain boundaries and γ/α phase boundaries. This steel plastically deforms via planar slip during the initial stages. The rather small γ grain size, coupled with the presence of hard α-ferrite particles, fosters the dynamic slip band refinement (DSBR) effect, thereby enhancing the flow stress sufficiently to trigger deformation twinning in the steel during the later stages. The DSBR effect, combined with the progressive formation of deformation twins, stacking faults and Lomer-Cottrell locks, imparts pronounced strain hardenability to this steel, leading to its outstanding ductility.

Abstract Refractory high-entropy alloys (HEAs) have attracted considerable attention due to their stable phase structure and excellent high-temperature properties. In this work, we performed first-principles calculations, coupled with experiments, to explore HEAs with high corrosion resistance. The results revealed that TiNbTa-based HEAs exhibited a lower tendency for corrosion. However, the appearance of local chemical fluctuations (LCFs) increased the corrosion tendency of TiNbTa-based HEAs. Comprehensive SHapley Additive exPlanations analyses uncovered that in a sample with configurational LCFs, the atomic order near the surface was altered. Therefore, corrosion behavior was affected. Based on experiments, the annealed samples exhibited typical chemical segregation and declined corrosion resistance.

Abstract A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Abstract Metal‐organic frameworks (MOFs), renowned for structural diversity and design flexibility, exhibit potential in catalysis. However, the pursuit of higher catalytic activity through defects often compromises stability, requiring a delicate balance. Traditional trial‐and‐error method for optimizing synthesis parameters within the complex chemical space is inefficient. Herein, taking the typical MOF UiO‐66(Ce) as an illustrative example, a closed loop workflow is built, which integrates machine learning (ML)‐assissted prediction, multi‐objective optimization (MOO) and experimental preparation to synergistically optimize the defect content and thermal stability of UiO‐66(Ce) for efficient hydrogenation of dicyclopentadiene (DCPD). An automatic data extraction program ensures data accuracy, establishing a high‐quality database. ML is employed to explore the intricate synthesis‐structure‐property correlations, enabling precise delineation of pure‐phase subspace and accurate predictions of properties. After two iterations, MOO model identifies optimal protocols for high defect content (>40%) and thermal stability (>300°C). The optimized UiO‐66(Ce) exhibits superior catalytic performance in hydrogenation of DCPD, validating the precision and reliability of our methodology. This ML‐assisted approach offers a valuable paradigm for solving the trade‐off riddle in materials field.

Abstract Future‐oriented Science & Technology (S&T) Strategies trigger the innovative developments of advanced materials, providing an envision to the significant progress of leading‐/cutting‐edge science, engineering, and technologies for the next few decades. Motivated by Made in China 2025 and New Material Power Strategy by 2035, several key viewpoints about automated research workflows for accelerated discovery and smart manufacturing of advanced materials in terms of AI for Science and main respective of big data, database, standards, and ecosystems are discussed. Referring to classical toolkits at various spatial and temporal scales, AI‐based toolkits and AI‐enabled computations for material design are compared, highlighting the dominant role of the AI agent paradigm. Our recent developed ProME platform together with its functions is introduced briefly. A case study of AI agent assistant welding is presented, which is consisted of the large language model, auto‐coding via AI agent, image processing, image mosaic, and machine learning for welding defect detection. Finally, more duties are called to educate the next generation workforce with creative minds and skills. It is believed that the transformation of knowledge‐enabled data‐driven integrated computational material engineering era to AI+ era promotes the transformation of smart design and manufacturing paradigm from “designing the materials” to “designing with materials.”

Among the reported photocatalysts, ZnIn2S4 has garnered significant research interest due to its advantageous layered structure and appropriate band gap. However, achieving rational design and effective interfacial regulation in heterojunctions remains challenging. In this study, we designed two novel heterojunctions: SrTiO3@ZnIn2S4 and SrCrO3@ZnIn2S4. The photocatalytic hydrogen evolution performance of prepared heterojunctions was systematically investigated under different single-wavelength light sources. Without a cocatalyst, the optimized hydrogen evolution efficiency of SrTiO3@ZnIn2S4 and SrCrO3@ZnIn2S4 reached 3.27 and 4.6 mmol g−1. The enhanced photocatalytic performance can be attributed to the formation of a type-II heterojunction, which improves light absorption capabilities and promotes the separation and transfer of photoinduced carriers. This study provides valuable insights into the strategic construction of heterojunctions for photocatalytic water splitting.

Abstract Finding high temperature superconductors (HTS) has been a continuing challenge due to the difficulty in predicting the transition temperature (Tc) of superconductors. Recently, the efficiency of predicting Tc has been greatly improved via machine learning (ML). Unfortunately, prevailing ML models have not shown adequate generalization ability to find new HTS, yet. In this work, a graph neural network model is trained to predict the maximal Tc (Tcmax) of various materials. Our model reveals a close connection between Tcmax and chemical bonds. It suggests that shorter bond lengths are favored by high Tc, which is in coherence with previous domain knowledge. More importantly, it also indicates that chemical bonds consisting of some specific chemical elements are responsible for high Tc, which is new even to the human experts. It can provide a convenient guidance to the materials scientists in search of HTS.

Abstract Accurately and quickly predicting hydrogen embrittlement performance is critical for the service of metal materials. However, due to multi‐source heterogeneity, existing hydrogen embrittlement data are missing, making it impractical to train reliable machine learning models. In this study, we proposed an ensemble learning training strategy for missing data based on the Adaboost algorithm. This method introduced a mask matrix with missing data and enabled each round of training to generate sub‐datasets, considering missing value information. The strategy first trained a subset of features based on the existing dataset and a selected method and continuously focused on the combination of features with the highest error for iterative training, where the mask matrix of the missing data was used as the input to fit the weights of each base learner using a neural network. Compared with directly modeling on highly sparse data, the predictive ability of this strategy was significantly improved by approximately 20%. In addition, in the testing of new samples, the predicted mean absolute error of the new model was successfully reduced from 0.2 to 0.09. This strategy offers good adaptability to the hydrogen embrittlement sensitivity of different sizes and can avoid interference from feature importance caused by filling data.

This study was conducted to investigate the effects of Bupleurum extract (BE) on the haematological profiles, the mineral and hormone levels of heat stressed dairy cows. Forty Holstein cows (75 ± 15 days in milk, 37.5 ± 1.8 kg of milk/d, and 1.7 ± 0.4 parity) with heat stress were randomly assigned to four treatments, which consisted of 0, 0.25, 0.5, or 1.0 g BE/kg DM (Dry Matter). Compared with the control group, cows that were fed 0.5 g/kg BE had a higher red blood cell count, haemoglobin, haematocrit, and white blood cell count. Compared with the control group, the supplementation of 0.25 and 0.5 g/kg BE decreased creatine kinase levels. Compared with the cows that were fed 0 or 1.0 g/kg BE, glutamic-oxaloacetic transaminase and alkaline phosphatase activities were lower in those given 0.5 g/kg BE, while serum glutamic-pyruvic transaminase was lower in those given 0.25 and 0.5 g/kg BE. Compared with the control group, the supplementation of 0.5 and 1.0 g/kg BE decreased sodium concentrations and increased potassium and calcium concentrations. In addition, the supplementation of 1.0 g/kg BE decreased phosphorus concentrations, while that of 0.5 g/kg BE increased chloride concentrations. As for hormone levels, triiodothyronine and prolactin levels increased in cows given 1.0 g/kg BE, while cortisol levels were lower in cows given 0.5 g/kg BE. Further, growth hormone levels were elevated in cows fed 0.25 g/kg BE. These findings suggest that supplemental BE at 0.5 g/kg could have positive effects on the blood metabolism of heat-stressed cows.HIGHLIGHTS Supplementation of 0.5 g/kg Bupleurum extract improved the haematological parameters of heat stressed dairy cows; Bupleurum extract can be used as a feed additive for cows.

Abstract The wide range of alloy composition controllability for multi-principal element alloys (MPEAs) may provide a great opportunity for discovering special forms of surface oxides to improve the corrosion and oxidation resistance in extreme environments. Changing the type and content of promoting passivation elements would not only change the microstructure of the alloy but also significantly affect the composition and structure of the surface passive film, resulting in a strong impact on the corrosion and oxidation resistance of the alloy. This article reviews recent research on the effects of alloying elements on the passivation properties, the contribution of each alloying element, and the synergistic effect between the elements on the passivation mechanisms and electrochemical dissolution characteristics of surface passive films that form on some MPEAs. In addition, the composition and structural characteristics of surface oxides relevant to the selective oxidation of elements are elaborated upon. Finally, several open questions and recommendations for research directions regarding the passivation and selective oxidation of MPEAs were provided to guide future exploration.

In recent years, the detrimental impact of traditional gas–liquid refrigerants on the environment has prompted a shift towards sustainable solid-state refrigeration technology. The elastocaloric effect, particularly in NiTi-based shape memory alloys (SMAs), presents a promising alternative due to its high coefficient of performance. However, conventional methods for alloy development are inefficient, often failing to meet the stringent requirements for practical applications. This study employed machine learning (ML) to accelerate the design of NiTi-based SMAs with an enhanced elastocaloric effect. Through active learning across four iterations, we identified nine novel NiTi-based SMAs exhibiting phase-transformation-induced entropy changes (ΔS) greater than 90 J/kg·K−1, surpassing most existing alloys. Our ML model demonstrates robust interpretability, revealing key relationships between material features and performance. This work not only establishes a more efficient pathway for alloy discovery but also aims to contribute significantly to the advancement of sustainable refrigeration technologies.

Abstract Alloy synthesis and processing determine the design of alloys with desired microstructure and properties. However, using data science to identify optimal synthesis-design routes from a specified set of starting materials has been limited by large-scale data acquisition. Text mining has made it possible to convert scientific text into structured data collections. Still, the complexity, diversity, and flexibility of synthesis and processing expressions, and the lack of annotated corpora with a gold standard severely hinder accurate and efficient extraction. Here we introduce a semi-supervised text mining method to extract the parameters corresponding to the sequence of actions of synthesis and processing. We automatically extract a total of 9853 superalloy synthesis and processing actions with chemical compositions from a corpus of 16,604 superalloy articles published up to 2022. These have then been used to capture an explicitly expressed synthesis factor for predicting γ′ phase coarsening. The synthesis factor derived from text mining significantly improves the performance of the data-driven γ′ size prediction model. The method thus complements the use of data-driven approaches in the search for relationships between synthesis and structures.

In material science and engineering, obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be difficult. In this work, we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system, i.e., (Ba1−x−yCaxSry)(Ti1−u−v−wZruSnvHfw)O3. The deep neural network model uses physical features as inputs and directly outputs the full spectrum, in addition to yielding the octahedral factor, Matyonov–Batsanov electronegativity, ratio of valence electron to nuclear charge, and core electron distance (Schubert) as four key descriptors. Owing to the physically meaningful features, our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid solutions. And the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each temperature. Furthermore, the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical information. The prediction is also experimentally validated by typical samples of (Ba0.85Ca0.15)(Ti0.98–xZrxHf0.02)O3. This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

Abstract Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize the γ′ volume fraction, size, and morphology in CoNiAlCr-based superalloys. The effectiveness of the algorithm is demonstrated by synthesizing alloys with desired γ/γ′ microstructure and optimizing γ′ microstructural parameters. In addition, the method leads to incorporating refractory elements to improve γ/γ′ microstructure in superalloys. After four iterations of experiments guided by the algorithm, we synthesize sixteen alloys of relatively high creep strength from ~120,000 candidates of which three possess high γ′ volume fraction (>54%), small γ′ size (77%).

Ferroelectric (FE) phase transition with a large polarization change benefits to generate large electrocaloric (EC) effect for solid-sate and zero-carbon cooling application. However, most EC studies only focus on the single-physical factor associated phase transition. Herein, we initiated a comprehensive discussion on phase transition in Pb0.99Nb0.02[(Zr0.6Sn0.4)1−xTix]0.98O3 (PNZST100x) antiferroelectric (AFE) ceramic system under the joint action of multi-physical factors, including composition, temperature, and electric field. Due to low energy barrier and enhanced zero-field entropy, the multi-phase coexistence point (x = 0.12) in the composition–temperature phase diagram yields a large positive EC peak of maximum temperature change (ΔTmax) = 2.44 K (at 40 kV/cm). Moreover, the electric field–temperature phase diagrams for four representative ceramics provide a more explicit guidance for EC evolution behavior. Besides the positive EC peaks near various phase transition temperatures, giant positive EC effects are also brought out by the electric field-induced phase transition from tetragonal AFE (AFET) to low-temperature rhombohedral FE (FER), which is reflected by a positive-slope boundary in the electric field–temperature phase diagram, while significant negative EC responses are generated by the phase transition from AFET to high-temperature multi-cell cubic paraelectric (PEMCC) with a negative-slope phase boundary. This work emphasizes the importance of phase diagram covering multi-physical factors for high-performance EC material design.

For efficient solid-state refrigeration technologies based on electrocaloric effect (ECE), it is a great challenge of simultaneously obtaining a large adiabatic temperature change (ΔT) within a wide temperature span (Tspan) in lead-free ferroelectric ceramics. Here, we studied the electrocaloric effect (ECE) in (1-x)(Na0.5Bi0.5)TiO3-xCaTiO3 ((1-x)NBT-xCT) and explored the combining effect of morphotropic phase boundary (MPB) and relaxor feature. The addition of CT not only constructs a MPB region with the coexistence of rhombohedral and orthorhombic phases, but also enhances the relaxor feature. The ECE peak appears around the freezing temperature (Tf), and shifts toward to lower temperature with the increasing CT amount. The directly measured ECE result shows that the ceramic of x = 0.10, which is in the MPB region, has an optimal ECE property of ΔTmax = 1.28 K @ 60 °C under 60 kV/cm with a wide Tspan of 65 °C. The enhanced ECE originates from the electric-field-induced transition between more types of polar nanoregions and long-range ferroelectric macrodomains. For the composition with more relaxor feature in the MPB region, such as x = 0.12, the ECE is relatively weak under low electric fields but it exhibits a sharp increment under a sufficiently high electric field. This work provides a guideline to develop the solid–state cooling devices for electronic components.

Abstract Corrosion initiation and propagation are a time-series problem, evolving continuously with corrosion time, and future pitting behavior depends closely on the past. Predicting localized corrosion for corrosion-resistant alloys remains a great challenge, as macroscopic experiments and microscopic theoretical simulations cannot couple internal and external factors to describe the pitting evolution from a time dimension. In this work, a data-driven method based on time-series analysis was explored. Taking cobalt-based alloys and duplex stainless steels as the case scenario, a corrosion propagation model was built to predict the free corrosion potential (Ecorr) using a long short-term memory neural network (LSTM) based on 150 days of immersion testing in saline solution. Compared to traditional machine learning methods, the time-series analysis method was more consistent with the evolution of ground truth in the Ecorr prediction of the subsequent 70 days’ immersion, illustrating that time-series dependency of pitting propagation could be captured and utilized.

In the context of global warming, heat stress has become one of the major stress factors limiting dairy cattle production. Although many methods have been explored to help cows mitigate the negative effects of heat stress during the hot summer months, maintaining the performance of high-yielding cows under heat stress is still a great challenge. The aim of this trial was to investigate the effect of RP-GABA in the diet on milk yield, milk composition and serum biochemical parameters in heat-stressed cows. Twenty Chinese Holstein cows in early lactation (51.00 ± 4.92 kg milk/d, 71 ± 10.94 d in milk and 2.68 ± 0.73 parities) were included in this experiment and randomly divided into four groups (n = 5/group). The four experimental groups consisted of one control group (0 g RP-GABA/d) and three treatment groups, given 5, 7.5 and 10 g RP-GABA/d of dry matter (DM) per cow, respectively. The results showed that supplementing high-yielding cows with 10 g/d of RP-GABA improved milk protein production but had no effect on the improvement of other production performance, the alleviation of heat stress in cows, or the improvement of immune function and antioxidant capacity. Ultimately, we conclude that the supplementation of 10 g/d RP-GABA to heat-stressed, high-yielding dairy cows can provide a degree of performance enhancement. Furthermore, our study provides some reference for nutritional improvement measures for summer heat stress in dairy cows, especially high-yielding cows.

Abstract Information Extraction (IE) in Natural Language Processing (NLP) aims to extract structured information from unstructured text to assist a computer in understanding natural language. Machine learning-based IE methods bring more intelligence and possibilities but require an extensive and accurate labeled corpus. In the materials science domain, giving reliable labels is a laborious task that requires the efforts of many professionals. To reduce manual intervention and automatically generate materials corpus during IE, in this work, we propose a semi-supervised IE framework for materials via automatically generated corpus. Taking the superalloy data extraction in our previous work as an example, the proposed framework using Snorkel automatically labels the corpus containing property values. Then Ordered Neurons-Long Short-Term Memory (ON-LSTM) network is adopted to train an information extraction model on the generated corpus. The experimental results show that the F1-score of γ’ solvus temperature, density and solidus temperature of superalloys are 83.90%, 94.02%, 89.27%, respectively. Furthermore, we conduct similar experiments on other materials, the experimental results show that the proposed framework is universal in the field of materials.

Abstract Introducing polarization field of piezoelectric materials is an effective strategy to improve photocatalytic performance. In this study, a new type of BaTiO3/CuO heterostructure catalyst was designed and synthesized to achieve high piezo-photocatalytic activity through the synergy of heterojunction and piezoelectric effect. The BaTiO3/CuO heterostructure shows a significantly enhanced piezo-photocatalytic degradation efficiency of organic pollutants compared with the individual BaTiO3 nanowires (NWs) and CuO nanoparticles (NPs). Under the co-excitation of ultrasonic vibration and ultraviolet radiation, the optimal degradation reaction rate constant k of polarized BaTiO3/CuO heterostructure on methyl orange (MO) dye can reach 0.05 min− 1, which is 6.1 times of photocatalytic rate and 7 times of piezocatalytic rate. The BaTiO3/CuO heterostructure with remarkable piezo-photocatalytic behavior provides a promising strategy for the development of high-efficiency catalysts for wastewater purification, and it also helps understand the coupling mechanism between piezoelectric effect and photocatalysis.

Abstract Data provides a foundation for machine learning, which has accelerated data-driven materials design. The scientific literature contains a large amount of high-quality, reliable data, and automatically extracting data from the literature continues to be a challenge. We propose a natural language processing pipeline to capture both chemical composition and property data that allows analysis and prediction of superalloys. Within 3 h, 2531 records with both composition and property are extracted from 14,425 articles, covering γ′ solvus temperature, density, solidus, and liquidus temperatures. A data-driven model for γ′ solvus temperature is built to predict unexplored Co-based superalloys with high γ′ solvus temperatures within a relative error of 0.81%. We test the predictions via synthesis and characterization of three alloys. A web-based toolkit as an online open-source platform is provided and expected to serve as the basis for a general method to search for targeted materials using data extracted from the literature.

Abstract The outstanding abilities of metamaterials to manipulate physical fields are extensively studied in both wave‐based and diffusion‐based fields. However, mass diffusion metamaterials, with the ability to manipulate diffusion with practical applications associated with chemical and biochemical engineering, have not yet been experimentally demonstrated. In this work, ion cloaking, concentrating, and selection in liquid solvents are verified by both simulations and experiments, and the concept of a “plug and switch” metamaterial is proposed based on scattering cancellation (SC) to achieve switchable functions by plugging modularized functional units into a functional motherboard. Plugging in any module barely affects the environmental diffusion field, but the module choice impacts different diffusion behaviors in the central region. Cloaking strictly hinds ion diffusion, and concentrating increase diffusion flux, while cytomembrane‐like ion selection permits the entrance of some ions but blocks others. In addition, these functions are demonstrated in special applications like the catalytic enhancement by the concentrator and the protein protection by the ion selector. This work not only experimentally demonstrates the effective manipulation of mass diffusion by metamaterials, but also shows that the “plug and switch” design is extensible and reconfigurable. It facilitates novel applications including sustained drug release, catalytic enhancement, bioinspired cytomembranes, etc.

Abstract With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative, the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community. In this paper, we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability. We call this architecture the Materials Genome Engineering Databases (MGED). MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers, unify data representation forms with user-centered presentation data model, and accelerate data discovery with advanced search capabilities. MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data. We describe MGED’s design, current status, and how MGED supports integrated management of shared data and services.

Dielectric resonators on metallic surface can enhance far-field scattering and boost near-field response having promising applications in nonlinear optics and reflection-type devices. However, the dependence of gap size between dielectric resonator and metallic surface on Mie resonant frequency is complex and desires a comprehensive physical interpretation. Here, we systematically study the effect of metallic substrate on the magnetic dipole (MD) resonant frequency at X-band by placing a high permittivity CaTiO3 ceramic block on metallic substrate and regulating their gap size. The simulated and experimental results show that there are two physical mechanisms to codetermine the metallic substrate-induced MD frequency. The greatly enhanced electric field pair in the gap and the coupling of MD resonance with its mirror image are decisive for small and large gaps, respectively, making the MD resonant frequency present an exponential blue shift first and then a slight red shift with increasing gap size. Further, we use the two mechanisms to explain different frequency shifting properties of ceramic sphere near metallic substrate. Finally, taking advantage of the sharp frequency shifting to small gaps, the ceramic block is demonstrated to accurately estimate the thickness or permittivity of thin film on metallic substrate through a governing equation derived from the method of symbolic regression. We believe that our study will help to understand the resonant frequency shifting for dielectric particle near metallic substrate and give some prototypes of ultrasensitive detectors.

Tuning the martensite transformation temperature through composition design has become an important way to broaden the applicable temperature range of shape memory alloys (SMAs). The empirical formula based on traditional statistics is a key reference for composition design. Due to the lack of experimental data, a large deviation may exist among the prediction results from the empirical formulas obtained by different data sources. In present work, we proposed an augmentation strategy of empirical formula based on a machine learning method to build the relationship between martensite transformation start temperature (Ms) and compositions in Cu-Al-based SMA system. A series of ML models were established by physical and chemical features and a Gaussian radial basis kernel function support vector machine (SVR.rbf) model was screened out based on mathematical and domain knowledge criteria. An augmented empirical formula of Ms as the function of compositions was fitted based on the abundant augmented dataset combined experimental data with predicted data by the SVR.rbf model. Compared with previous empirical formula fitted by small experimental dataset, the accuracy and robustness of the augmented empirical formula was significantly improved without additional experimental cost. This strategy offers a recipe to build empirical formula based on a small experimental dataset.

The purpose of this study is to investigate the correlation between intergranular stress corrosion cracking (IGSCC) and grain boundary characteristics in brass. The Volta potential and nature of the grain boundaries in H62 brass were studied by scanning Kelvin probe force microscopy and Electron Backscattered Diffraction, respectively. The α/β phase boundaries and random grain boundaries with low work functions can act as anodic areas to promote IGSCC propagation. Moreover, due to a combination of the inherent characteristics and high work function of Σ3 annealing twin boundaries (ATBs), IGSCC is successfully halted at a triple junction containing Σ3 ATB.

Rice straw is an important roughage resource for ruminants in many rice-producing countries. In this study, a rice brittle mutant (BM, mutation in OsCesA4, encoding cellulose synthase) and its wild type (WT) were employed to investigate the effects of a cellulose synthase gene mutation on rice straw morphological fractions, chemical composition, stem histological structure and in situ digestibility. The morphological fractions investigation showed that BM had a higher leaf sheath proportion (43.70% vs 38.21%, p0.05) was detected in neutral detergent fiber (NDFom) and ADL contents for both strains. Histological structure observation indicated that BM stems had fewer sclerenchyma cells and a thinner sclerenchyma cell wall than WT. The results of in situ digestion showed that BM had higher DM, NDFom, cellulose and hemicellulose disappearance at 24 or 48 h of incubation (p