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Abstract In this article, we propose a query-and-learn active learning approach combined with first-principles calculations to rapidly search for potentially stable crystal structure via elemental substitution, to clarify their stabilization mechanism, and integrate this approach to SmFe$$_{12}$$ 12 -based compounds with ThMn$$_{12}$$ 12 structure, which exhibits prominent magnetic properties. The proposed method aims to (1) accurately estimate formation energies with limited first-principles calculation data, (2) visually monitor the progress of the structure search process, (3) extract correlations between structures and formation energies, and (4) recommend the most beneficial candidates of SmFe$$_{12}$$ 12 -substituted structures for the subsequent first-principles calculations. The structures of SmFe$$_{12-\upalpha -\upbeta }\mathsf {X}_{\upalpha }\mathsf {Y}_{\upbeta }$$ 12 - α - β X α Y β before optimization are prepared by substituting $$\mathsf {X}, \mathsf {Y}$$ X , Y elements—Mo, Zn, Co, Cu, Ti, Al, Ga—in the region of $$\upalpha +\upbeta α + β < 4 into iron sites of the original SmFe$$_{12}$$ 12 structures. Using the optimized structures and formation energies obtained from the first-principles calculations after each active learning cycle, we construct an embedded two-dimensional space to rationally visualize the set of all the calculated and not-yet-calculated structures for monitoring the progress of the search. Our machine learning model with an embedding representation attained a prediction error for the formation energy of $$1.25\times 10^{-2}$$ 1.25 × 10 - 2 (eV/atom) and required only one-sixth of the training data compared to other learning methods. Moreover, the time required to recall most potentially stable structures was nearly four times faster than the random search. The formation energy landscape visualized using the embedding representation revealed that the substitutions of Al and Ga have the highest potential to stabilize the SmFe$$_{12}$$ 12 structure. In particular, SmFe$$_{9}$$ 9 [Al/Ga]$$_{2}$$ 2 Ti showed the highest stability among the investigated structures. Finally, by quantitatively measuring the change in the structures before and after optimization using OFM descriptors, the correlations between the coordination number of substitution sites and the resulting formation energy are revealed. The negative-formation-energy-family SmFe$$_{12-\upalpha -\upbeta }$$ 12 - α - β [Al/Ga]$$_{\upalpha }\mathsf {Y}_{\upbeta }$$ α Y β structures show a common trend of increasing coordination number at substituted sites, whereas structures with positive formation energy show a corresponding decreasing trend. Impact statement Seeking the next generation of high-performance magnets is a crucial demand for replacing the widely accepted Nd-Fe-B magnets developed in the middle 80s. The iron-rich compounds with the original tetragonal ThMn12 structure appear as the most potential candidates except for the hard synthesizing it in nature due to its high energy of formation. Stabilization for this material system is expected by substituting new elements, but the vast number of possible structures makes the exploration difficult even for theoretical calculations. This article proposes an integration of first-principles calculations and explainable active learning to efficiently explore the crystal structure space of this material system. In particular, the explored crystal structure space can be rationally visualized, on which the relationship between substitution elements, substitution sites, and crystal structure stabilization can be intuitively interpreted.

Interface modulation of nickel phosphide (Ni

We investigate the correlation between geometrical information, stability, and magnetization of SmFe[Formula: see text]-based structures using machine learning-aided genetic algorithm structure generation and first-principle calculation. In parallel with structure generation inherited using the USPEX program, a pool of structures is created for every population using the sub-symmetry perturbation method. A framework using embedded orbital field matrix representation as structure fingerprint and Gaussian process as a predictor has been applied to ranking the most potential stability structures. As a result, the original structure SmFe[Formula: see text] with the well-known tetragonal [Formula: see text] symmetry is investigated with a parabolic dependence between formation energy and its magnetization by continuous distortions of the unit-cell lattice parameter and individual sites. Notably, a SmFe[Formula: see text] structure with [Formula: see text] symmetry is found with 7.5[Formula: see text] increasing magnetization while keeping the similar formation energy with the most stable structures in this family. With SmFe[Formula: see text]CoN family, structures with N interstitial position in the center of Sm and Fe octahedron show outperform all other structures in both ability of stabilization and remaining high magnetization of the original structure. Finally, further investigation using metric learning embedding space brings valuable insight into the correlation between geometrical arrangement, stability, and magnetization of this structure family.

Measuring the similarity between materials is essential for estimating their properties and revealing the associated physical mechanisms. However, current methods for measuring the similarity between materials rely on theoretically derived descriptors and parameters fitted from experimental or computational data, which are often insufficient and biased. Furthermore, outliers and data generated by multiple mechanisms are usually included in the dataset, making the data-driven approach challenging and mathematically complicated. To overcome such issues, we apply the Dempster–Shafer theory to develop an evidential regression-based similarity measurement (eRSM) method, which can rationally transform data into evidence. It then combines such evidence to conclude the similarities between materials, considering their physical properties. To evaluate the eRSM, we used two material datasets, including 3[Formula: see text] transition metal–4[Formula: see text] rare-earth binary and quaternary high-entropy alloys with target properties, Curie temperature, and magnetization. Based on the information obtained on the similarities between the materials, a clustering technique is applied to learn the cluster structures of the materials that facilitate the interpretation of the mechanism. The unsupervised learning experiments demonstrate that the obtained similarities are applicable to detect anomalies and appropriately identify groups of materials whose properties correlate differently with their compositions. Furthermore, significant improvements in the accuracies of the predictions for the Curie temperature and magnetization of the quaternary alloys are obtained by introducing the similarities, with the reduction in mean absolute errors of 36% and 18%, respectively. The results show that the eRSM can adequately measure the similarities and dissimilarities between materials in these datasets with respect to mechanisms of the target properties.

Catalyst degradation at the cathode of a membrane electrode assembly (MEA) remains a critical issue for practical polymer electrolyte fuel cell (PEFC) operation, but such wet systems impede detaile...

Oxygen storage and release with oxygen diffusion in the bulk of the cerium–zirconium solid solution oxide Ce2Zr2Ox (x = 7–8), which possesses an atomically ordered arrangement of cerium and zirconium atoms, is the key to three-way exhaust catalysis. Oxygen storage proceeds via heterogeneous oxygen diffusion into the vacant sites of Ce2Zr2O7 particles, but the heterogeneous oxygen diffusion track is erased after oxygen storage in the Ce2Zr2Ox bulk. Here we show three-dimensional hard X-ray spectro-ptychography to clearly visualize the three-dimensional cerium valence map in Ce2Zr2Ox particles, and unsupervised learning reveals the concealed oxygen-diffusion-driven three-dimensional nanoscale cerium oxidation behavior and tracking areas inside individual mixed-oxide particles during the oxygen storage process. The described approach may permit the nanoscale chemical imaging of reaction tracking areas in solid materials.

In this study, we investigate the structure-stability relationship of hypothetical Nd-Fe-B crystal structures using descriptor-relevance analysis and the t-SNE dimensionality reduction method. 149 hypothetical Nd-Fe-B crystal structures are generated from 5967 LA-T-X host structures in Open Quantum Materials Database by using the elemental substitution method, with LA denoting lanthanides, T denoting transition metals, and X denoting light elements such as B, C, N and O. A hypothetical crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe, and all light element sites with B. High-throughput first-principle calculations are applied to evaluate the phase stability of these structures. Twenty of them are found to be potentially formable. The descriptor-relevance analysis on the orbital field matrix (OFM) materials' descriptor reveals the average atomic coordination number as the essential factor in determining the structure stability of these substituted Nd-Fe-B crystal structures. 19 among 20 hypothetical structures that are found potentially formable have an average coordination number larger than 6.5. In addition, all the local structures represented by the OFM descriptors are integrated into a visible space to study the detailed correlation between their characteristics and the stability of the crystal structure to which they belong. We discover that unstable substituted structures frequently carry Nd and Fe local structures with two prominent points: low average coordination numbers and fully occupied B neighboring atoms. Moreover, there are only three popular forms of B local structures appearing on all potentially formable substituted structures: cage networks, planar networks, and interstitial sites. The discovered relationships are promising to speed up the screening process for the new formable crystal structures., Comment: 12 pages, 8 figures

Solution-processed poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) films have great potential for organic optoelectronic applications due to their high throughput, low cost, and mechanical flexibility. However, the strong acidity and insulating nature of PSS causes a crucial deterioration of device performance. Here, we present an efficient route for addressing the limitations of PSS by molecular manipulation via incorporation of nitrogen-doped carbon quantum dots (N-CQDs) into PEDOT:PSS. The incorporation of N-CQDs significantly reduces resistance, increases work function, modifies the surface energy and smooths the surface morphology of PEDOT:PSS. In our density functional theory (DFT) calculations, inclusion of N-CQDs weakens the electrostatic interaction between PEDOT and PSS, resulting in molecular manipulation of PEDOT:PSS. Compared to conventionally oxidized CQDs (O-CQDs), N-CQDs have better effects in PEDOT:PSS matrices, including π−π stacking with PEDOT and hydrophilic interaction with PSS, which can effectively induce molecular reorientation. The enhancement of charge extraction and charge injection performance of N-CQD-incorporated PEDOT:PSS are demonstrated via organic optoelectronic devices including organic photovoltaics and organic light-emitting diode with significant improvements in power conversion efficiency and quantum efficiency, respectively. Molecular manipulation via the incorporation of N-CQDs holds promise for the development of highly efficient PEDOT:PSS hole-transporting layers in various optoelectronic devices.

Electrospinning and subsequent carbonization of ternary polymer blends led to many interesting and important composition-dependent characteristics. Ternary blends consisting of polyacrylonitrile (PAN), poly(methyl methacrylate) (PMMA), and poly(N-vinyl-2-pyrrolidone) (PVP) exhibited unique phase separation behaviors, which were attributed to the disparities in intermolecular interactions among the constituent polymer pairs. In addition to this phase behavior, combination of the individual characteristics of each polymer as a carbon precursor led to unique morphologies and microstructural properties of the carbonized nanofiber products (CNPs). Representatively, use of PMMA as a continuous phase in the blend generated a bundle-like morphology, while the combination of PAN and PMMA/PVP as a continuous phase and dispersed phases, respectively, led to the development of open, effective porosity. These morphological and microstructural properties were correlated with the electrochemical properties of the CNPs. ...

Carbon quantum dots (CQDs) have attracted extreme interest as a promising nanocarbon platform for divergence optoelectronics due to their high stability, good dispersibility in solvents, and tunable optical and electronic properties. Herein, planar p-i-n type perovskite solar cells (PSCs) with enhanced efficiency and long-term stability were developed by incorporating CQDs into a nickel oxide (NiO) hole transport layer (HTL). The incorporation of CQDs downshifts the band structure of NiO, leading to good alignment with the work-function of the tin-doped indium oxide (ITO) electrode and the band-edges of the perovskite. The efficient cascade charge transport achieved with the optimized incorporation ratio of CQDs resulted in an enhanced power conversion efficiency (PCE) of 17.02%, compared to that of the PSC fabricated with bare NiO (15.66%), even though they were fabricated in air. The suppressed charge recombination accompanied by restricted charge accumulation curtails the J-V hysteresis, with a reduction from 4.5% to less than 1%. Moreover, long-term stability under atmospheric conditions without any encapsulation was achieved with CQD-incorporated NiO. More than 70% of the initial PCE was retained over 190 h. This work suggests a novel strategy for fabricating solution-processible metal oxide interlayers with highly efficient charge migration for divergence energy conversion devices.

In this paper, we present a fraudulent email detection model using advanced feature choice. We extracted various kinds of features and compared the performance of each category of features with the others in terms of the fraudulent email detection rate. The different types of features are incorporated step by step. The detection of fraudulent email has been considered as a classification problem and it is evaluated using various state-of-the art algorithms and on CCM (Nizamani et al., 2011) [1] which is authors’ previous cluster based classification model. The experiments have been performed on diverse feature sets and the different classification methods. The comparison of the results is also presented and the evaluation show that for the fraudulent email detection tasks, the feature set is more important regardless of classification method. The results of the study suggest that the task of fraudulent emails detection requires the better choice of feature set; while the choice of classification method is of less importance.

A facile route to graphene/polymer hydrogel nanofibers was developed. An aqueous dispersion of graphene (containing >40% bilayer graphene flakes) stabilized by a functionalized water-soluble polymer with phenyl side chains was successfully electrospun to yield nanofibers. Subsequent vapor-phase cross-linking of the nanofibers produced graphene-embedded hydrogel nanofibers (GHNFs). Interestingly, the GHNFs showed chemical sensitivity to the cationic dyes methylene blue (MB) and crystal violet (CV) in the aqueous phase. The adsorption capacities were as high as 0.43 and 0.33 mmol g–1 s–1 for MB and CV, respectively, even in a 1.5 mL s–1 flow system. A density functional theory calculation revealed that aqueous-phase MB and CV dyes were oriented parallel to the graphene surface and that the graphene/dye ensembles were stabilized by secondary physical bonding mechanisms such as the π–π stacking interaction in an aqueous medium. The GHNFs exhibited electrochemical properties arising mainly from the electric do...

The Curie temperature (T C) of RT binary compounds consisting of 3d transition-metal (T ) and 4f rare-earth elements (R) is analyzed systematically by a developed machine learning technique called kernel regression-based model evaluation. Twenty-one descriptive variables were designed assuming completely obtained information of the T C. Multiple kernel regression analyses with different kernel types: cosine, linear, Gaussian, polynomial, and Laplacian kernels were implemented and examined. All possible descriptive variable combinations were generated to construct the corresponding prediction models. As a result, by appropriate combinations between descriptive variable sets and kernel formulations, we demonstrate that a number of kernel regression models can accurately reproduce the T C of the RT compounds. The relevance of descriptive variables for predicting T C are systematically investigated. The results indicate that the rare-earth concentration is the most relevant variable in the T C phenomenon. We demonstrate that the regression-based model selection technique can be applied to learn the relationship between the descriptive variables and the actuation mechanism of the corresponding physical phenomenon, i.e., T C in the present case.

We propose a data-driven method to extract dissimilarity between materials, with respect to a given target physical property. The technique is based on an ensemble method with Kernel ridge regression as the predicting model; multiple random subset sampling of the materials is done to generate prediction models and the corresponding contributions of the reference training materials in detail. The distribution of the predicted values for each material can be approximated by a Gaussian mixture models. The reference training materials contributed to the prediction model that accurately predicts the physical property value of a specific material, are considered to be similar to that material, or vice versa. Evaluations using synthesized data demonstrate that the proposed method can effectively measure the dissimilarity between data instances. An application of the analysis method on the data of Curie temperature ( T C ) of binary 3d transition metal- 4f rare-earth binary alloys also reveals meaningful results on the relations between the materials. The proposed method can be considered as a potential tool for obtaining a deeper understanding of the structure of data, with respect to a target property, in particular.

Hydrogen sulfide is a critical biological messenger, but few biologically compatible methods are available for its detection in vivo. Here, we describe the design and synthesis of a novel azide-functionalized near-infrared probe, NIR-Az, for a hydrogen sulfide assay in which a self-immolative linker is incorporated between the azide moiety and phenolic dihydroxanthene fluorophore from a cyanine dye. A large "turn-on" near-infrared fluorescence signal results from the reduction of the azide group of the fluorogenic moiety to an amine, in which the self-immolative linker also enhances the accessibility of NIR-Az to hydrogen sulfide. NIR-Az can select hydrogen sulfide from among 16 analytes, including cysteine, glutathione, and homocysteine. By exploiting the superior properties of NIR-Az, such as its good biocompatibility and rapid cell internalization, we successfully demonstrated its usefulness in monitoring both the concentration- and time-dependent variations of hydrogen sulfide in living cells and animals (detection limit less than 0.26μM), thereby providing a powerful approach for probing hydrogen sulfide chemistry in biological systems.

Conducting polymers (CPs) have been widely studied to realize advanced technologies in various areas such as chemical and biosensors, catalysts, photovoltaic cells, batteries, supercapacitors, and others. In particular, hybridization of CPs with inorganic species has allowed the production of promising functional materials with improved performance in various applications. Consequently, many important studies on CPs have been carried out over the last decade, and numerous researchers remain attracted to CPs from a technological perspective. In this review, we provide a theoretical classification of fabrication techniques and a brief summary of the most recent developments in synthesis methods. We evaluate the efficacy and benefits of these methods for the preparation of pure CP nanomaterials and nanohybrids, presenting the newest trends from around the world with 205 references, most of which are from the last three years. Furthermore, we also evaluate the effects of various factors on the structures and properties of CP nanomaterials, citing a large variety of publications.