jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fan Yang | Yisha Xia | Jiawei Wang | Pengbo Wang | Hui Jiang
Source: IEEE Transactions on Magnetics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Improved Prediction Algorithm for Noise of Transformer Considering Material Parameter Uncertainty This study proposes a rapid transformer noise prediction method to quantify the propagation of electrical steel material uncertainties in transformer systems. To characterize uncertainties in silicon steel, stochastic models of magnetization and magnetostriction behaviors are developed using vine copula theory, generating correlated sample curves across flux densities ranging from 0.1-1.9 T. To mitigate computational costs, an adaptive differential evolution-optimized backpropagation neural network (ADE-BP) is introduced for transformer noise analysis. The proposed method is implemented to the finite element model of a transformer to validate its effectiveness and accuracy. Numerical results reveal a 12.7% probability of exceeding permissible noise thresholds due to variations in magnetic parameters, thereby underscoring the necessity for topology optimization in high-efficiency, low-noise transformer design methodologies. Transformer ; Magnetostriction ; Backpropagation ; Linear variable differential transformer ; Rotary variable differential transformer ; Artificial neural network ; Electrical steel ; Finite element method ; Magnetic flux Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Advanced Electrical Measurement Techniques
Authors: Zimian Luo | Bai Xue | Lan Xie | Margulan Ibraimov | Zhenmin Cheng | Qing Chen | Shaohua Lin | Yue Wu | Ai Peng | Jie Kong
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Interpretable Deep Learning Workflow Coupling Residual-Connected Multilayer Perceptron with Gradient Boosting Trees for Design of Electromagnetic Interference Shielding MXene-Filled Composites EMI ; Electromagnetic shielding ; Electromagnetic interference ; Gradient boosting ; Robustness (evolution) ; Boosting (machine learning) ; Perceptron ; Multilayer perceptron ; Deep learning Electromagnetic wave absorption materials ; Electromagnetic Fields and Biological Effects ; MXene and MAX Phase Materials
Authors: S. M. Chaudhari | Cristóbal Méndez | R. J. Choudhary | Tathagata Banerjee | Maciej Olszewski | Jadrien T. Paustian | Jae‐Hong Choi | Zhaslan Baraissov | Rafael Hernández | David A. Muller | B. L. T. Plourde | Gregory D. Fuchs | Valla Fatemi | T. A. Arias
Source: ArXiv.org (preprint, 2025-10-02)
Field: Materials Science — Materials Chemistry

Active-Learning Inspired $\textit{Ab Initio}$ Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide formation beneath different capping materials. This approach identifies Zr, Hf, and Ta as effective diffusion barriers. Our analysis further reveals that the oxide formation energy per oxygen atom serves as an excellent standalone descriptor for predicting barrier performance. By combining this new descriptor with lattice mismatch as a secondary criterion to promote structurally coherent interfaces, we identify Zr, Ta, and Sc as especially promising candidates. This closed-loop strategy integrates first-principles theory, machine learning, and limited experimental data to enable rational design of next-generation materials. Niobium ; Oxide ; Qubit ; Lattice (music) ; Superconductivity ; Atom (system on chip) ; Set (abstract data type) ; Quantum tunnelling ; Energy (signal processing) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Hossny, Karim
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Decision Tree Insights Analytics (DTIA): An Explainable AI Framework for Severe Accident Analysis In nuclear reactor safety analysis, we label an accident as severe once a partial core meltdown and material relocation begin. Researchers use simulation tools such as ANSYS and MELCOR to study these events safely, producing vast and complex datasets. In this work, we applied machine learning explainability and interpretability to extract insights from severe accident simulations for the Nordic boiling water reactor (BWR) through five iterative studies. First, we examined the explainability of the decision tree classification algorithm to distinguish between accident types using time-wise pressure vessel external temperature. Second, we generalised the model to create a more statistically robust and generic framework, introducing the open-source Decision Tree Insights Analytics (DTIA) framework (https://github.com/KHossny/DTIA), which combines explainability, interpretability, and statistical robustness. Third, we applied DTIA to high-dimensional MELCOR COR package data for a station blackout combined with a loss-of-coolant accident (SBO + LOCA) in a Nordic BWR, revealing new findings. Fourth, we used DTIA to compare structural variables of the reactor pressure vessel lower head under SBO and SBO + LOCA conditions. Finally, we coupled DTIA with K-Means clustering to address its need for labelled data, uncovering previously overlooked events such as canister melting. We concluded that the patterns identified by machine learning in mapping inputs to outputs can uncover insights that were previously overlooked, particularly in high-dimensional and complex datasets. Interpretability ; Analytics ; Decision tree ; Blackout ; Boiling water reactor ; Accident (philosophy) ; Reactor pressure vessel ; Accident management ; Cluster analysis Nuclear Materials and Properties ; Nuclear Engineering Thermal-Hydraulics ; Risk and Safety Analysis
Authors: Jeong
Min Choi (2937270) | Jaehoon Kim (1410382) | Ji-Hwan Lee (2193526) | Won-Joon Son (2158957) | Seungwu Han (1802209)
Source: Figshare (article, 2025-12-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomistic Insightsinto Cu/Amorphous-Ta<sub><i>x</i></sub>N Interfacial Adhesionvia Machine Learning InteratomicPotentials: Effects of Stoichiometry and Interface Construction Accurate understanding and control of interfacial adhesion between Cu and Ta<sub><i>x</i></sub>N diffusion barriers are essential for ensuring the mechanical reliability and integrity of Cu interconnect systems in semiconductor devices. Amorphous tantalum nitride (a-Ta<sub><i>x</i></sub>N) barriers are particularly attractive due to their superior barrier performance, attributed to the absence of grain boundaries. However, a systematic atomistic investigation of how varying Ta stoichiometries influences adhesion strength at Cu/a-Ta<sub><i>x</i></sub>N interfaces remains lacking, hindering a comprehensive understanding of interface optimization strategies. In this study, we employ machine learning interatomic potentials (MLIPs) to perform steered molecular dynamics (SMD) simulations of Cu/a-Ta<sub><i>x</i></sub>N interfaces. We simultaneously evaluate three distinct interface construction approachesstatic relaxation, high-temperature annealing, and simulated Cu depositionto comprehensively investigate their influence on adhesion strength across varying Ta compositions (<i>x</i> = 1, 2, 4). Peak force and work of adhesion values from SMD simulations quantitatively characterize interface strength, while atomic stress and strain analyses elucidate detailed deformation behavior, highlighting the critical role of interfacial morphologies. Additionally, we explore the atomistic mechanisms underlying cohesive failure, revealing how targeted incorporation of Ta atoms into Cu layers enhances the cohesive strength of the interface. This study demonstrates how MLIP-driven simulations can elucidate atomic-scale relationships between interface morphology and adhesion behavior, providing insights that can guide future atomistic engineering strategies toward enhancing intrinsic barrier adhesion, potentially enabling liner-free interconnect technologies. Molecular dynamics ; Interatomic potential ; Adhesion ; Deformation (meteorology) ; Diffusion barrier ; Interface (matter) ; Stress (linguistics) ; Diffusion Copper Interconnects and Reliability ; Electronic Packaging and Soldering Technologies ; 3D IC and TSV technologies
Authors: Yan, Qimin | Li, Tongcang | Gao, Xingyu | Vaidya, Sumukh | Dikshit, Saakshi | Luo, Yue | Strauf, Stefan | Moukaouine, Reda | Pershin, Anton | Gali, Adam | Fang, Zhenyao | Stanfield, Harvey | Vera-Marun, Ivan J. | Newburger, Michael | Singh, Simranjeet | Zhu, Tiancong | Brotons-Gisbert, Mauro | Jöns, Klaus D. | Gerardot, Brian D. | Kim, Brian S. Y. | Schaibley, John R. | Seyler, Kyle L. | Balgley, Jesse | Hone, James | Fong, Kin Chung | Wang, Lin | Burkard, Guido | Zeng, Yihang | Heindel, Tobias | Ateş, Serkan | Vogl, Tobias | Aharonovich, Igor
Source: arXiv (Cornell University) (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

Roadmap: 2D Materials for Quantum Technologies Two-dimensional (2D) materials have emerged as a versatile and powerful platform for quantum technologies, offering atomic-scale control, strong quantum confinement, and seamless integration into heterogeneous device architectures. Their reduced dimensionality enables unique quantum phenomena, including optically addressable spin defects, tunable single-photon emitters, low-dimensional magnetism, gate-controlled superconductivity, and correlated states in Moiré superlattices. This Roadmap provides a comprehensive overview of recent progress and future directions in exploiting 2D materials for quantum sensing, computation, communication, and simulation. We survey advances spanning spin defects and quantum sensing, quantum emitters and nonlinear photonics, computational theory and data-driven discovery of quantum defects, spintronic and magnonic devices, cavity-engineered quantum materials, superconducting and hybrid quantum circuits, quantum dots, Moiré quantum simulators, and quantum communication platforms. Across these themes, we identify common challenges in defect control, coherence preservation, interfacial engineering, and scalable integration, alongside emerging opportunities driven by machine$-$learning$-$assisted design and integrated experiment$-$theory feedback loops. By connecting microscopic quantum states to mesoscopic excitations and macroscopic device architectures, this Roadmap outlines a materials-centric framework for integrating coherent quantum functionalities and positions 2D materials as foundational building blocks for next-generation quantum technologies. Quantum technology ; Quantum sensor ; Quantum ; Quantum imaging ; Quantum network ; Quantum information science ; Quantum computer ; Quantum nanoscience ; Quantum information 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Graphene research and applications
Authors: H. Li | A. Oh | Olivier Allegre | Patrick S. Salter | O. De Aguiar Francisco | M. Gersabeck | Patrick Parkinson | C. Smith | Nawal Al‐Amairi
Source:  (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Tpa Characterisation and Neural Network to Improve Spatial and Temporal Resolution of 3D Diamond Detectors 3D diamond detectors are an advanced form of diamond detector for particle detection. It uses graphitic paths produced by femtosecond laser induced graphitisation inside chemical vapour deposition diamond samples to serve as the electrodes. To improve the detector performance, the electrodes can have tailored 3D structures. In this work, Two Photon Absorption technique, together with results obtained at CERN SPS test beam are used to characterise the performances of 3D diamond detectors with various electrode structures. Based on the experimental data and simulation results from Sentaurus TCAD and Garfield++, a Deep Neural Network based data processing algorithm is trained to extract physical information from the induced current waveform, which shows the improvement in spatial and timing resolution of 3D diamond detectors. Diamond ; Detector ; Chemical vapor deposition ; Image resolution ; Femtosecond ; Temporal resolution ; Electrode ; Artificial neural network Diamond and Carbon-based Materials Research ; Particle Detector Development and Performance ; Photocathodes and Microchannel Plates
Authors: Wei Zhao | Ze Zhang | Tianhao Li | Yuanfei Yang | Chao Zhang | Kaiwen Wu | Jianhui Dong | Zilong Wang | Yang Luo | Dunbo Yu
Source: Materials Today Communications (article, 2025-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Active transfer learning for optimizing anisotropic injection-moulded magnets Magnet ; Active learning (machine learning) ; Process (computing) ; Interpretability ; Orientation (vector space) ; Heat transfer ; Efficient energy use ; Anisotropy Magnetic Properties of Alloys ; Electric Motor Design and Analysis ; Magnetic Properties and Applications
Authors: Christian Pfaendner (21001700) | Viktoria Korn (13583572) | Pritom Gogoi (21001703) | Benjamin Unger (15095117) | Kristyna Pluhackova (1847899)
Source: Figshare (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

ART-SM: BoostingFragment-Based Backmapping by MachineLearning In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future. Dihedral angle ; Molecular dynamics ; Boosting (machine learning) ; Resolution (logic) ; Autoregressive model ; Process (computing) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Guo‐Feng Fan | Jun Qian | Li‐Ling Peng | Xin-Hang Jia | Luwei Zuo | Jia-An Yan | J. Chen | Anantkumar J. Umbarkar | Wei‐Chiang Hong
Source: Computer Modeling in Engineering & Sciences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Optimizing Performance Prediction of Perovskite Photovoltaic Materials by Statistical Methods-Intelligent Calculation Model Accurate prediction of perovskite photovoltaic materials’ optoelectronic properties is crucial for developing efficient and stable materials, advancing solar technology. To address poor interpretability, high computational complexity, and inaccurate predictions in relevant machine learning models, this paper proposes a novel methodology. The technical route of this paper mainly centers on the random forest-knowledge distillation-bidirectional gated recurrent unit with attention technology (namely RF-KD-BIGRUA), which is applied in perovskite photovoltaic materials. Primarily, it combines random forest to quantitatively assess feature importance, selecting variables with significant impacts on photoelectric conversion efficiency. Subsequently, statistical techniques analyze the weight distribution of variables influencing power conversion efficiency (PCE, %) to extract key features. In the model optimization phase, knowledge distillation transfers features from complex teacher models to student models, enhancing prediction accuracy. Additionally, Bidirectional Gated Recurrent Unit with Attention technology (BiGRU-Attention) is introduced to further optimize predictive performance while substantially reducing computational costs. The results demonstrate that integrating statistical techniques into intelligent optimization models can quantify photovoltaic system uncertainties and reduce prediction errors before experimental fabrication, enabling efficient pre-fabrication screening of perovskite materials that meet energy-storage criteria and providing accurate guidance for material selection. Photovoltaic system ; Perovskite (structure) ; Key (lock) ; Random forest ; Statistical learning ; Performance prediction ; Distillation ; Statistical model Machine Learning in Materials Science ; Perovskite Materials and Applications ; Solar Radiation and Photovoltaics
Authors: Guoyang Shi (21403974) | Haoyu Guo (9253039) | Tianchu Gao (19933851) | Haoping Wu (8693169) | Si-Heng Luo (18064777) | Sanlei Wang (21403977) | Yong Lei (337331) | Yun Zhang (131894) | Yangfan Xie (21403980) | Bin Ren (352410) | Zhong-Qun Tian (1313955) | Gui-Fang Shao (8876984) | Guo-Kun Liu (2285356)
Source: Figshare (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Unveiling Spectrum–StructureCorrelation inVibrational Spectroscopy: Task-Driven Deep Learning ClassificationBalancing Global Fusion and Local Extraction Spectrum–structure correlation is crucial to identify and quantify chemicals, in which classification of mixtures and identification of functional groups are two central tasks. Deep learning-driven algorithms have made significant strides to these two tasks. However, many of these algorithms are merely adaptations of models originally designed for computer vision applications. As a result, the models often suffer from either low accuracy or limited generality when applied to spectral data due to the overlooked inherent limitations in feature richness and volume of spectral data. Here, in light of the distinctive difference in the attention of global and local information in spectral data between these two tasks, we developed contrapuntally two CNN-based algorithms, incorporating multiscale convolution and attention mechanism, to address the unique requirements of each task. It was found that the lightweight CNN-Peak algorithm is favored for the classification of a mixture, a type of single-label task, in which the feature fusion of global information is more important. Meanwhile, the more complex ResNet-ResPeak algorithm is ideally suited for the identification of functional groups, a type of multilabel task, in which the feature extraction of local information takes precedence. The task-oriented, conceptual design of deep learning algorithms not only enhances the efficacy and accuracy of spectrum–structure correlation analysis but also feeds back to achieve a more rigorous experimental design and implementation, forming a closed loop of AI for Science. Convolution (computer science) ; Pattern recognition (psychology) ; Feature extraction ; Generality ; Feature (linguistics) ; Deep learning ; Identification (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: А. О. Еркимбаев | V. Yu. Zitserman | Г. А. Кобзев
Source: Automatic Documentation and Mathematical Linguistics (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Tradition of Data-Intensive Use: The Example of Domain Thermophysics. Methods and Algorithms Using the example of thermophysics, the evolution of approaches to working with scientific data on substances and materials properties is traced. This paper shows that across all stages, thermophysics can be classified as a data-intensive science, characterized by a focus on working with data, including its storage, its organization, and the extraction of meaningful information. It presents improvements in processing methods that are associated with the use of new information technologies, including machine learning techniques. Their potential for the field of thermodynamics relative to traditional statistical methods is analyzed. In this context, the general issue of the relationship between statistics and data science which has generated extensive debate in literature and online is discussed. All the authors’ conclusions are based on an analysis of specific issues related to the prediction of the properties of substances and constructing the equation of state and thermodynamic models for multicomponent systems. Domain (mathematical analysis) ; Field (mathematics) ; Focus (optics) ; State (computer science) ; Information extraction ; Data processing Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Catalysis and Oxidation Reactions
Authors: Ton M. Blackshaw | Joseph C. Davies | Kristian Spoerer | Jonathan D. Hirst
Source: Journal of Chemical Information and Modeling (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

Enhancing Monte Carlo Tree Search for Retrosynthesis Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm. The enhancements were deployed in AiZynthFinder and have been integrated into the open-source electronic lab notebook, AI4Green, available at https://ai4green.app. A memory-efficient stock file was used to reduce the computational carbon footprint. Both enhancements significantly reduced, by up to 50%, the computational clock-time to solve 1500 heavy (500-800 Da) molecules. The dUCT enhancement increased the number of routes found per molecule for the 1500 heavy molecules and a 50,000-molecule set from ChEMBL. eUCT and dUCT-v2 solved between 600 and 900 more molecules than the unenhanced MCTS algorithm across the 50,000 molecules. When limited to a 150 s time constraint, dUCT-v1 solved ∼5 million more routes to the 50,000 targets than the unenhanced algorithm. Monte Carlo method ; Retrosynthetic analysis ; Tree (set theory) ; Monte Carlo tree search ; Computer science ; Mathematics ; Statistics ; Chemistry ; Combinatorics Machine Learning in Materials Science ; Constraint Satisfaction and Optimization ; Artificial Intelligence in Games