Abstract Earth history is punctuated by voluminous magmatism and the formation of large igneous provinces (LIPs). Although anomalous mantle temperatures are known to be involved in many LIPs formation, the potentially critical role of fluids remains elusive. Here we apply machine learning methods (e.g., random forest, deep neural network, and support vector machines) to train models based on global datasets of basalts associated with different settings. The trained models predict that the basalts of Tarim LIP in northwestern China show a spatial decrease in their island arc affinity from northeast to southwest, which can be correlated to fluids released from earlier southward oceanic subduction. Temporally, the fluid activity declined from 290 Ma basalts to 270 Ma mafic dykes, suggesting that the fusible components in the mantle source were waning over time and ultimately a strengthened lithosphere was generated. Our study provides new insights into the crucial role of fluids in the generation of LIPs, particularly those related to ancient subducted slabs.
Abstract Pyrite is a ubiquitous mineral in many ore deposits and sediments, and its trace element composition is mainly controlled by temperature, oxygen fugacity, pH, compositions of fluids, and host rock composition. A discriminant analysis (DA), based on multi-element compositions of pyrite, distinguishes iron oxide-apatite (IOA), iron oxide copper-gold (IOCG), skarn Cu-(Fe), porphyry Cu-Mo, orogenic Au, volcanic-hosted massive sulfide (VMS), sedimentary exhalative (SEDEX) deposits, and barren sedimentary pyrite. Testing of the DA classifier yields an accuracy of 98% for IOA, 96% for IOCG, 91% for skarn Cu-(Fe), 94% for porphyry Cu-Mo, 87% for orogenic Au, 84% for VMS, 96% for SEDEX, and 85% for barren sedimentary pyrite. Furthermore, neural network, support vector machine, and random forest, were performed for selecting the optimum classifier more accurately. In these three techniques, the support vector machine yielded the highest overall accuracy (98% for IOA, IOCG, skarn Cu-Fe, and porphyry Cu-Mo, and 97% for orogenic Au, VMS, SEDEX, and barren sedimentary pyrite) and thus is an appropriate technique in predicting pyrite types.
Abstract Niobium‐based tungsten bronze oxides have recently emerged as attractive fast‐charging anodes for lithium‐ion batteries (LIBs), owing to their structural openings and adjustability. However, electrodes with tungsten bronze structures usually suffer from structural variability induced by Li + intercalation/de‐intercalation, leading to unsatisfactory cycling performance. To circumvent this limitation, a novel tetragonal tungsten bronze (TTB) structure, Ba 3.4 Nb 10 O 28.4 (BNO), is developed as an anode material for LIBs with prominent cycling performance. An unprecedented cation‐vacancy ordered superstructure with a periodic distribution of active and inactive sites is revealed inside the BNO. Through multiple characterizations and theoretical studies, it is demonstrated that this superstructure can improve the lithium‐ion diffusion and disperse the structural strain induced by Li + ‐intercalation to enable stable Li + ‐storage. Benefiting from the superstructure‐induced local structural stability, both the BNO bulk and Ba 3.4 Nb 10 O 28.4 @C (BNO@C) microspheres can deliver >90% capacity retention after 250 cycles at 2 C and close to 90% capacity retention after 2000 cycles at 10 C. These results are of significant importance for establishing the structure–property relationship between the cation‐vacancy ordered superstructure and Li + ‐storage performance, facilitating the rational design of stable tungsten bronze anodes.
Abstract In the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.
Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24. • A stacked deep learning multi-kernel learning model (SD-MKL) for flyrock prediction. • Use stacked-representation learning (S-RL) framework to achieve deep learning. • Use the multi-kernel learning model with multi-feature fusion strategy to learn the relationship between generated feature and original feature. • Use boosting method to improve the prediction accuracy of SD-MKL.
Anisotropy is a significant and prevalent characteristic of materials, conferring orientation-dependent properties, meaning that the creation of original symmetry enables key functionality that is not found in nature. Even with the advancements in atomic machining, synthesis of separated symmetry in different directions within a single structure remains an extraordinary challenge. Here, we successfully fabricate NiS ultrafine nanorods with separated symmetry along two directions. The atomic structure of the nanorod exhibits rotational symmetry in the radial direction, while its axial direction is characterized by divergent translational symmetry, surpassing the conventional crystalline structures known to date. It does not fit the traditional description of the space group and the point group in three dimensions, so we define it as a new structure in which translational symmetry and rotational symmetry are separated. Further corroborating the atomic symmetric separation in the electronic structure, we observed the combination of stripe and vortex magnetic domains in a single nanorod with different directions, in accordance with the atomic structure. The manipulation of nanostructure at the atomic level introduces a novel approach to regulate new properties finely, leading to the proposal of new nanotechnology mechanisms.
Basalts are ubiquitous mafic rocks found within diverse tectonic settings on Earth. Despite concerted efforts to distinguish tectonic settings through the chemical compositions of basalt, some features of these rocks related to tectonic processes can be obscured by weathering and erosion over geological time, making the discrimination results ambiguous. In this study, we utilized major and trace element data of clinopyroxene in basalts from five distinct tectonic settings: continental within-plate basalts (WPB), island arc basalts (IAB), ocean island basalts (OIB), oceanic floor basalts (OFB), and continental flood basalts (CFB). Employing three machine learning techniques—Multi-layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF)—we aim to discriminate tectonic settings and magma affinities. Sparse Multinomial Regression (SMR) approach is used to quantitatively discern geochemical signatures that are distinctive of each tectonic setting. The outcomes reveal the efficacy of SVM, which attains an accuracy of 92.1% (major element-based) and 95.2% (major and trace elements-based) for tectonic discrimination. Furthermore, SVM achieves an accuracy of 92.9% (major elements-based) and 95.7% (major and trace elements-based) for magma affinity discrimination. Our study shows that the integration of electron microprobe data from clinopyroxene with machine learning techniques provides an effective approach to distinguish various tectonic settings and magma affinities. The classifier models have been applied to investigate the Neoproterozoic geodynamics of the Jiangnan Orogen. The models show the two pulses of mantle plume events (∼830 Ma and ∼785 Ma) triggering break-up of the Rodinia supercontinent.
Abstract Distinct skyrmion phases at room temperature hosted by one material offer additional degree of freedom for the design of topology-based compact and energetically-efficient spintronic devices. The field has been extended to low-dimensional magnets with the discovery of magnetism in two-dimensional van der Waals magnets. However, creating multiple skyrmion phases in 2D magnets, especially above room temperature, remains a major challenge. Here, we report the experimental observation of mixed-type skyrmions, exhibiting both Bloch and hybrid characteristics, in a room-temperature ferromagnet Fe 3 GaTe 2 . Analysis of the magnetic intensities under varied imaging conditions coupled with complementary simulations reveal that spontaneous Bloch skyrmions exist as the magnetic ground state with the coexistence of hybrid stripes domain, on account of the interplay between the dipolar interaction and the Dzyaloshinskii-Moriya interaction. Moreover, hybrid skyrmions are created and their coexisting phases with Bloch skyrmions exhibit considerably high thermostability, enduring up to 328 K. The findings open perspectives for 2D spintronic devices incorporating distinct skyrmion phases at room temperature.
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