jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhen Li | He Gao | Zirui Mao | Bo Zhu | Lei Sun | Xuefei Bi | Yuanwei Zhu | Yongsen Han | Daomin Min | Ji Liu | Shengtao Li
Source: Journal of Physics D Applied Physics (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

The mechanism of EP/SiC coating modulated DC flashover characteristics of epoxy composites in SF<sub>6</sub>/N<sub>2</sub> mixtures Abstract Surface flashover is an inevitable insulation issue for basin-type insulators in gas-insulated switchgears/lines, which significantly challenges the reliability of the electrical power systems. Previous studies have indicated that polymer/semiconductor-filler composite coatings effectively improve the insulation properties; however, the influence mechanism of the coating materials on flashover has not been demonstrated from a molecular perspective. In this work, epoxy/silicon-carbide (EP/SiC) composites were coated onto an EP substrate. The energy-level structure, surface trap, surface charging, and DC flashover voltage in SF 6 /N 2 were calculated and characterized, and the process by which the tailored molecular energy level influences surface charge transport and flashover characteristics was elucidated. The incorporating of SiC particles reduced the width of the bandgap and introduced shallow traps, which improved carrier mobility and surface conductivity. Quantitative analysis of charge transport indicated that the improved carrier mobility and reduced surface trap level accelerated the surface charge dissipation. This reduced the tangential electrical field distortion and surface charge density and further impeded gas ionization. When the SiC concentration was 15 wt%, the flashover performance improved by 20.88%. This study describes the mechanism by which the EP/SiC coating regulates the surface charge distribution to improve the surface flashover performance by establishing a relationship among the microscopic molecular energy-level structures, mesoscopic charge transport, and macroscopic discharge phenomena. Epoxy ; Composite material ; Materials science ; Coating ; Arc flash ; Mechanism (biology) ; Insulator (electricity) High voltage insulation and dielectric phenomena ; Semiconductor materials and devices ; Copper Interconnects and Reliability
Authors: Yeqin Li | Yan Zhang | Liwen Tian | Ju Li | Hui‐Hua Li | Ximing Wang | Cuiyan Wang
Source: European Radiology Experimental (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study • 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer. Medicine ; Receiver operating characteristic ; Malignancy ; Breast cancer ; Neuroradiology ; Stage (stratigraphy) ; Pathological ; Differential diagnosis ; Nuclear medicine ; Radiology ; Magnetic resonance imaging ; Cancer ; Pathology ; Internal medicine Lanthanide and Transition Metal Complexes ; MRI in cancer diagnosis ; Advanced MRI Techniques and Applications
Authors: Xinran Liu | Yuzhe Lu | Ali Abbasi | Meiyi Li | Javad Mohammadi | Soheil Kolouri
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Teaching Networks to Solve Optimization Problems Leveraging machine learning to facilitate the optimization process is an emerging field that promises to bypass the fundamental computational bottleneck caused by classic iterative solvers in critical applications requiring near-real-time optimization. Most existing approaches focus on learning data-driven optimizers that lead to fewer iterations in solving an optimization. In this paper, we take a different approach and propose to replace the iterative solvers altogether with a trainable parametric set function, that outputs the optimal arguments/parameters of an optimization problem in a single feed-forward. We denote our method as Learning to Optimize the Optimization Process ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LOOP</i> ). We show the feasibility of learning such parametric (set) functions to solve various classic optimization problems, including linear/nonlinear regression, principal component analysis, transport-based coreset, and quadratic programming in supply management applications. In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LOOP</i> . Finally, through various numerical experiments, we show that the trained solvers could be orders of magnitude faster than the classic iterative solvers while providing near-optimal solutions. Computer science Machine Learning in Materials Science ; Machine Learning and Algorithms ; Machine Learning and Data Classification
Authors: Songyuan Li | Xiaopeng Wang | Han Yue | Jiangang Feng | Zhen‐Yu Yin | Jiawang Yang | Wan Luo | Jun Xie
Source: Symmetry (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Partial Discharge Data Enhancement and Pattern Recognition Method Based on a CAE-ACGAN and ResNet In order to address the issue of the accuracy of partial discharge pattern recognition being constrained by unbalanced samples and the deep structure of the deep learning network, a method for partial discharge data enhancement and pattern recognition based on a convolutional autoencoder auxiliary classifier generative adversarial network (CAE-ACGAN) and a residual network (ResNet) is proposed. The initial step involves the preprocessing of the acquired partial discharge signals, with the phase resolved partial discharge (PRPD) spectra subsequently employed as the training samples. Secondly, a CAE-ACGAN is constructed. The model combines the advantages of a deep convolutional self-coding structure and a generative adversarial paradigm to generate high-quality phase resolved partial discharge spectrograms. Subsequently, a ResNet is employed as the classifier for partial discharge pattern recognition, utilising the CAE-ACGAN-enhanced partial discharge dataset for network training to achieve accurate recognition of partial discharge signals. The experimental findings demonstrate that the SSIM and PSNR indexes of the CAE-ACGAN model utilised in this study are 0.92 and 45.88 dB, respectively. The partial discharge pattern method employing the CAE-ACGAN and ResNet exhibits superiority in identifying partial discharges, attaining an identification accuracy of 98%, which is 7.25% higher than the pre-enhancement level. Partial discharge ; Pattern recognition (psychology) ; Artificial intelligence ; Generative adversarial network ; Classifier (UML) ; Autoencoder ; Computer science ; Deep learning ; Preprocessor ; Residual ; Algorithm ; Engineering High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Anomaly Detection Techniques and Applications
Authors: Chunxu Qin | Xiaokai Zhu | Pengfei Zhu | Wenjie Lin | Liqiang Liu | Chuanqiang Che | Huijuan Liang | Huichun Hua
Source: Sensors (article, 2024-05-27)
Field: Materials Science — Materials Chemistry

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm-Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM. Cross-linked polyethylene ; Partial discharge ; Polyethylene ; Composite number ; SIGNAL (programming language) ; Composite material ; Materials science ; Acoustics ; Structural engineering ; Electrical engineering ; Engineering ; Computer science ; Voltage ; Physics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Debojyoti Das | Debdutta Chakraborty
Source: Journal of Computational Chemistry (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Physicochemical Properties in Lithium‐Ion Battery Electrolytes With Active Learning Applied to Graph Neural Networks Accurate prediction of physicochemical properties, such as electronic energy, enthalpy, free energy, and average vibrational frequencies, is critical for optimizing lithium-ion battery (LIB) performance. Traditional methods like density functional theory (DFT) are computationally expensive and inefficient for large-scale screening. In this study, we apply active learning on top of graph neural networks (GNNs) to efficiently predict these properties. By focusing on uncertain data points, active learning reduces training data size while maintaining high accuracy. Applied to the LIBE and MPcules datasets, the model achieved an R-squared (R<sup>2</sup>) values of 0.9977 with a mean absolute error (MAE) of 9.66 Ha for electronic energy and an R<sup>2</sup> values of 0.957 with an MAE of 13.94 cm<sup>-1</sup> for average vibrational frequencies. SHapley Additive exPlanations (SHAP) provided insights into key features influencing predictions, such as atomic number and spin multiplicity. This approach enhances both predictive accuracy and model interpretability, offering a scalable solution for LIB electrolyte discovery. Artificial neural network ; Computer science ; Density functional theory ; Graph ; Lithium (medication) ; Machine learning ; Battery (electricity) ; Ion ; Artificial intelligence ; Chemistry ; Computational chemistry ; Theoretical computer science ; Thermodynamics ; Physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Vadim Korolev | Artem Mitrofanov
Source: Journal of Chemical Information and Modeling (article, 2024-03-08)
Field: Chemistry — Inorganic Chemistry

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design. Computer science ; Artificial neural network ; Graph ; Theoretical computer science ; Artificial intelligence ; Algorithm Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Ankur K. Gupta | Miko M. Stulajter | Yusuf Shaidu | Jeffrey B. Neaton | Wibe A. de Jong
Source: ACS Omega (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Equivariant Neural Networks Utilizing Molecular Clusters for Accurate Molecular Crystal Lattice Energy Predictions Equivariant neural networks have emerged as prominent models in advancing the construction of interatomic potentials due to their remarkable data efficiency and generalization capabilities for out-of-distribution data. Here, we expand the utility of these networks to the prediction of crystal structures consisting of organic molecules. Traditional methods for computing crystal structure properties, such as plane-wave quantum chemical methods based on density functional theory (DFT), are prohibitively resource-intensive, often necessitating compromises in accuracy and the choice of exchange-correlation functional. We present an approach that leverages the efficiency, and transferability of equivariant neural networks, specifically Allegro, to predict molecular crystal structure energies at a reduced computational cost. Our neural network is trained on molecular clusters using a highly accurate Gaussian-type orbital (GTO)-based method as the target level of theory, eliminating the need for costly periodic DFT calculations, while providing access to all families of exchange-corelation functionals and post-Hartree-Fock methods. The trained model exhibits remarkable accuracy in predicting lattice energies, aligning closely with those computed by plane-wave based DFT methods, thus representing significant cost reductions. Furthermore, the Allegro network was seamlessly integrated with the USPEX framework, accelerating the discovery of low-energy crystal structures during crystal structure prediction. Crystal structure prediction ; Equivariant map ; Density functional theory ; Artificial neural network ; Computer science ; Gaussian ; Statistical physics ; Lattice (music) ; Hybrid functional ; Crystal structure ; Crystal (programming language) ; Algorithm ; Physics ; Artificial intelligence ; Topology (electrical circuits) ; Theoretical computer science ; Molecule ; Quantum mechanics ; Chemistry ; Mathematics ; Crystallography Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: Ashiqur Rasul | Md Shafayat Hossain | Ankan Ghosh Dastider | Himaddri Shakhar Roy | Md. Zahid Hasan | Quazi D. M. Khosru
Source: Scientific Reports (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

A machine learning based classifier for topological quantum materials Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology with graph neural network which offers an accuracy of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>91.4</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> and an F1 score of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>88.5</mml:mn> <mml:mo>%</mml:mo></mml:mrow> </mml:math> in classifying topological versus non-topological materials, outperforming the other state-of-the-art classifier models. Additionally, out-of-distribution and newly discovered topological materials can be classified using our method with high confidence. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their crystalline structures and thus proved to be an effective method to represent and process non-Euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the proposed neural network integrates a topological analysis of crystal structures into the deep learning model, enhancing both robustness and performance. Our classifier can serve as an efficacious tool for predicting the topological class, thereby enabling a high-throughput search for fascinating topological materials. Classifier (UML) ; Computer science ; Artificial intelligence ; Quantum ; Machine learning ; Topology (electrical circuits) ; Physics ; Mathematics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Zhao Zhang | Yinghe Wang | Shu Li | Shuai Li | Minghua Chen
Source: Advanced Theory and Simulations (article, 2024-06-05)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Prediction of Voltage and Specific Capacity for Electrode Materials Abstract The application of machine learning (ML) is becoming widespread and playing an important role in the property prediction and design of battery. Average voltage and specific capacity are two important indicators of electrode materials, which determine suitability of electrode materials in batteries. In this study, 4351 data are collected from Material Project and 324 features are extracted from Material Project or calculated by Matminer . Four ML models are discussed for prediction of average voltage and specific capacity. In order to ensure the prediction accuracy and efficiency of ML models, sequential backward selection (SBS) method is introduced to select optimal feature set, which can reduce redundancy features. The combination of Deep Neural Network model and optimal feature set selected by the SBS method achieves accurate prediction and outperforms Crystal Graph Convolutional Neural Network and Graph‐attention Graph Neural Network. The interpretability analysis provides insight into the relationship between features and two target properties. To cope with the poor prediction performance for metal‐ion battery with scarce data, transfer learning is adopted and an excellent improvement is achieved in the prediction in Na‐ion, Mg‐ion and Ca‐ion electrode material. It can be concluded that ML is an effective approach to battery property prediction and design. Electrode ; Computer science ; Voltage ; Artificial intelligence ; Machine learning ; Materials science ; Electrical engineering ; Chemistry ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: Le Tang | Guozhen Zhang | Jun Jiang
Source: Chinese Journal of Chemical Physics (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

Machine learning approach accelerates search for solid state electrolytes In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage, solid-state battery technology is attracting much research and attention. Solid-state electrolytes, as the key component of next-generation battery technology, are favored for their high safety, high energy density, and long life. However, finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications. Focusing on inorganic solid-state electrolytes, this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity, good thermal stability, and structural and phase stability. Traditional experimental and theoretical computational methods suffer from inefficiency, thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics. Through the gradient descent-based XGBoost algorithm, we successfully predicted the energy band structure and stability of the materials, and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously, which greatly accelerated the development of solid-state batteries. Computer science ; State (computer science) ; Solid-state ; Artificial intelligence ; Machine learning ; Engineering ; Engineering physics ; Programming language Machine Learning in Materials Science
Authors: Axel Gomez | Miguel de la Puente | Rolf David | Damien Laage
Source: Comptes Rendus Chimie (article, 2024-05-15)
Field: Materials Science — Materials Chemistry

Neural network potentials for exploring condensed phase chemical reactivity Recent advances in machine learning offer powerful tools for exploring complex reaction mechanisms in condensed phases via reactive simulations. In this tutorial review, we describe the key challenges associated with simulating reactions in condensed phases, we introduce neural network potentials and detail how they can be trained. We emphasize the importance of active learning to construct the training set, and show how these reactive force fields can be integrated with enhanced sampling techniques, including transition path sampling. We illustrate the capabilities of these new methods with a selection of applications to chemical reaction mechanisms in solution and at interfaces. Reactivity (psychology) ; Phase (matter) ; Chemistry ; Nanotechnology ; Chemical physics ; Materials science ; Organic chemistry ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods