jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abhilash Purohit | Sekaran Sathees Kumar | S. Padmanabhan | Priyabrat Pradhan | Gaurav Gupta | Pravat Ranjan Pati
Source: Materialwissenschaft und Werkstofftechnik (article, 2025-09-01)
Field: Materials Science — Polymers and Plastics

Computational modelling and optimization of mechanical properties of red brick dust‐epoxy composites using machine learning approach Utilizing construction waste as reinforcement in polymer composites is becoming more popular these days. In the current research, composites with varying amounts of red brick dust in an epoxy matrix are developed. The mechanical characteristics of these composites have been assessed. As the filler content increases, a progressive rise in impact strength and microhardness values is noted. However, as compared to neat epoxy, epoxy‐red brick dust composites show a slight reduction in tensile and flexural strength. A maximum hardness of 30.82 HV 2 and impact strength of 25.7 kJ/m 2 are obtained by adding 30 wt. ‐ % of red brick dust to the epoxy matrix; nevertheless, the tensile and flexural strengths are determined to be 23.37 MPa and 10.37 MPa, respectively. The impact strength of neat polymer increased by 43.57 % with an addition 30 wt. ‐ % filler. Simple linear regression is used to compare the results of mechanical properties. The machine learning simple linear regression model's projected outcomes closely match the mechanical values. Mechanical behaviors of the composites are also investigated with the help of Digimat software and compared with the investigational results. It has been noted that the experimental results are in excellent agreement with the numerically predicted values. Flexural strength ; Ultimate tensile strength ; Epoxy ; Brick ; Filler (materials) ; Universal testing machine ; Indentation hardness Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes
Authors: Yu Yan | Guoqing Geng
Source: Cement and Concrete Research (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Quantification of GGBS hydration using deep learning - A comparison with SEM-EDS mapping, PONKCS XRD and isothermal calorimetry methods Isothermal process ; Isothermal titration calorimetry ; Calorimetry ; Materials science ; Differential scanning calorimetry ; Ground granulated blast-furnace slag ; Composite material ; Analytical Chemistry (journal) ; Chemistry ; Thermodynamics ; Physical chemistry ; Chromatography ; Physics ; Cement X-ray Diffraction in Crystallography ; Metallurgical Processes and Thermodynamics ; Non-Destructive Testing Techniques
Authors: Haidong Wang | Min Wang | Baishun Li | Xin Ji
Source: Materials Letters (article, 2025-01-27)
Field: Materials Science — Metals and Alloys

Machine learning prediction of mechanical properties in stir zone of 310 austenitic steel friction stir welds Materials science ; Austenite ; Friction stir welding ; Metallurgy ; Friction stir processing ; Microstructure ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Advanced Welding Techniques Analysis
Authors: Brittney Gorman | Catelynn C. Shafer | Nagarjunachary Ragi | Kumar Sharma | Elizabeth K. Neumann | Christopher Anderton
Source: Nature Reviews Nephrology (review, 2025-03-27)
Field: Chemistry — Spectroscopy

Imaging and spatially resolved mass spectrometry applications in nephrology Computational biology ; Omics ; Proteomics ; Metabolomics ; Mass spectrometry imaging ; Genomics ; Medicine ; Computer science ; Bioinformatics ; Mass spectrometry ; Data science ; Biology ; Genome ; Chemistry ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Xiaodong Wang | Liwei Ni | Cheng Zhang | Qiyue Xu | Shuliang Ye
Source: Infrared Physics & Technology (article, 2025-02-19)
Field: Chemistry — Analytical Chemistry

Using a one-dimensional convolutional neural network on FTIR spectroscopy to measure the thickness of composite plastic films Fourier transform infrared spectroscopy ; Measure (data warehouse) ; Composite number ; Spectroscopy ; Materials science ; Convolutional neural network ; Composite material ; Analytical Chemistry (journal) ; Optics ; Computer science ; Artificial intelligence ; Physics ; Chemistry ; Data mining ; Chromatography ; Astronomy Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Alejandra Arroyo-Cerezo | Ana M. Jiménez–Carvelo | Miriam Medina‐García | Esteban A. Roca-Nasser | Luis Cuadros-Rodrı́guez
Source: TrAC Trends in Analytical Chemistry (article, 2025-01-30)
Field: Chemistry — Analytical Chemistry

Rediscovering similarity analysis of analytical signal: A not well-known mainstay of chemometrics The search for tools to extract information from analytical signals has led to increasingly complex solutions. Multivariate analysis continues to evolve, exploring new techniques to acquire knowledge. Similarity analysis offers a simple yet powerful tool for comparing analytical signals, aiding in the development of machine learning models for product authentication, quality control, and chemical information mining. When working with 2D signals, like chromatograms or spectra, similarity analysis can be addressed through pairwise or dataset comparisons. Pairwise comparisons use similarity indices, while dataset comparisons typically employ exploratory data analysis. This review highlights the importance of similarity analysis in chemometrics and proposes a classification of different strategies: (i) distance-based approaches, (ii) error-based approaches, (iii) spatial orientation-based approaches, (iv) correlation-based methods, and (v) information entropy-based approaches, aiming to create a consensus and provide analysts with an indispensable tool. • An overview of similarity analysis strategies used in chemometrics is outlined. • Strategies for pairwise and dataset 2D analytical signals comparison are discussed. • Several applications are reviewed, highlighting the value of similarity analysis. • These approaches enhance data interpretation and support analytical decisions. Chemometrics ; Similarity (geometry) ; SIGNAL (programming language) ; Pattern recognition (psychology) ; Computer science ; Chromatography ; Chemistry ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Methods and Models
Authors: Kai Zhang | Shuo Wang | Jumin Qiu | Muyi Yang | Tingting Liu | Shuyuan Xiao | Isabelle Staude | Thomas Pertsch | Yu Wang | Chengjun Zou
Source: Advanced Optical Materials (article, 2025-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Momentum‐Space Tunable Metasurfaces for Switchable Image Processing Abstract The exceptional ability of optical metasurfaces to manipulate light has enabled integrated analog computing and image processing in ultracompact, energy‐efficient platforms that support high speeds. To date, metasurfaces have demonstrated various analog processing functions, including differentiation, convolution, and classification. However, a fundamental limitation of existing designs is their static functionality, which restricts adaptability to diverse application scenarios. To address this challenge, momentum‐space reconfigurable metasurfaces operating in the near‐infrared range are experimentally demonstrated, capable of switchable image processing functions including image differentiation and bright‐field imaging. These meta‐devices are achieved by integrating nematic liquid crystals with silicon metasurfaces that support resonances of quasi‐bound states in the continuum (quasi‐BICs). The quasi‐BIC modes enable further design freedom over the angular dispersion of metasurfaces. The results showcase an electrically tunable, CMOS‐compatible approach to reconfigurable optical computing, offering significant potential for applications such as online training of diffractive neural networks, machine vision, and augmented reality. Materials science ; Space (punctuation) ; Optics ; Optoelectronics ; Nanotechnology ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Advanced Wireless Communication Technologies
Authors: Jun Li | Wenqi Fang | Shangjian Jin | Chenyu Suo | Tengdong Zhang | Yanling Wu | Xiaodan Xu | Yong Liu | Dao‐Xin Yao
Source: AI for Science (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

A deep learning approach to search for superconductors from electronic bands The prediction of superconducting transition temperatures ( $T_\mathrm{c}$ ) from electronic band structures remains challenging due to the complex interplay of electronic correlations, lattice dynamics, and symmetry. Here, we develop DeeperBand , a symmetry-aware 3D Vision Transformer model, to decode band structure features linked to superconductivity. By integrating density functional theory calibrated bands with an attention mechanism, DeeperBand identifies steep Fermi-level density-of-states slopes and flat bands as key indicators, aligning with Bardeen, Cooper, and Schrieffer and van Hove singularity paradigms. Trained on 2474 samples with symmetry-informed augmentation (1362 superconductors and 1112 non-superconductors), the model achieves R ^2 = 0.887 for $T_\mathrm{c}$ prediction. Crucially, independent validation using 23 newly synthesized superconductors (published post-training) demonstrates robust generalization across diverse material classes. High-throughput screening of 46 442 materials predicts 15 026 potential superconductors ( $T_\mathrm{c}\,\gt\,$ 2 K), including 1502 candidates ( $T_\mathrm{c}\,\gt\,$ 10 K) such as noble-metal oxides (e.g. BaAgO _2 ) with cuprate-like band features. The DeeperBand model can help identifying band structure features linked to superconductivity through interpretable attention maps. This work bridges machine learning with superconductivity theory, establishing data-driven band structure analysis as a powerful paradigm for targeted material discovery with preserved physical interpretability. Superconductivity ; Deep learning ; Materials science ; Condensed matter physics ; Artificial intelligence ; Computer science ; Psychology ; Physics Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Iron-based superconductors research
Authors: Yuxing Ma | Hongyu Yu | Yang Zhong | Shimao Chen | Xingao Gong | Hongjun Xiang
Source: Applied Physics Letters (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Transferable machine learning approach for predicting electronic structures of charged defects The study of electronic properties of charged defects plays a crucial role in advancing our understanding of how defects influence conductivity, magnetism, and optical behavior in various materials. However, despite its significance, research on large-scale defective systems has been hindered by the high computational cost associated with density functional theory (DFT). In this study, we propose HamGNN-Q, an E(3) equivariant graph neural network framework capable of accurately predicting DFT Hamiltonian matrices for diverse point defects with varying charges, utilizing a unified set of network weights. By incorporating background charge features into the element representation, HamGNN-Q facilitates a direct mapping from structural characteristics and background charges to the electronic Hamiltonian matrix of charged defect systems, obviating the need for DFT calculations. We showcase the model's high precision and transferability by evaluating its performance on GaAs systems encompassing diverse charged defect configurations. Furthermore, we predicted the wave function distribution of polarons induced by defects. We analyzed the node features through principal component analysis, providing physical insights for the interpretability of the HamGNN-Q model. Our approach provides a practical solution for accelerating electronic structure calculations of neutral and charged defects and advancing the design of materials with tailored electronic properties. Computer science ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Chi-Huan Tung | Lijie Ding | Guan-Rong Huang | Lionel Porcar | Yuya Shinohara | Bobby G. Sumpter | Changwoo Do | Wei‐Ren Chen
Source: Journal of Applied Crystallography (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Insights into distorted lamellar phases with small-angle scattering and machine learning Lamellar phases are essential in various soft matter systems, with topological defects significantly influencing their mechanical properties. In this report, we present a machine-learning approach for quantitatively analyzing the structure and dynamics of distorted lamellar phases using scattering techniques. By leveraging the mathematical framework of Kolmogorov-Arnold networks, we demonstrate that the conformations of these distorted phases - expressed as superpositions of complex waves - can be reconstructed from small-angle scattering intensities. Through the contour analysis of wave field phase singularities, we obtain the statistics of the spatial distribution of topological defects. Furthermore, we establish that the temporal evolution of these defects can be derived from the time-dependent traveling wave field, informed by the dispersion relation of spectral components. This method opens new avenues for investigating the dynamics of distorted lamellar phases using various dynamic scattering techniques such as neutron spin echo and X-ray photon correlation spectroscopy. These findings enhance our microscopic understanding of how defects influence the physical properties of lamellar materials, with implications for both equilibrium and non-equilibrium states in general lamellar systems. Lamellar structure ; Scattering ; Small-angle scattering ; Materials science ; Small-angle X-ray scattering ; Crystallography ; Optics ; Chemistry ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Geology and Paleoclimatology Research
Authors: Matthieu J. Verstraete | João Abreu | Guillaume E. Allemand | Bernard Amadon | Gabriel Antonius | Maryam Azizi | Lucas Baguet | Clémentine Barat | Louis Bastogne | Romuald Béjaud | Jean-Michel Beuken | Jordan Bieder | Augustin Blanchet | François Bottin | J. Bouchet | Julien Bouquiaux | Éric Bousquet | James Boust | Fabien Brieuc | Véronique Brousseau-Couture | Nils Brouwer | Fabien Bruneval | Aloïs Castellano | Emmanuel Castiel | Jean-Baptiste Charraud | Jean Clérouin | Michel Côté | Clément Duval | Alejandro Gallo | Frédéric Gendron | Grégory Geneste | Philippe Ghosez | Matteo Giantomassi | Olivier Gingras | Fernando Gómez‐Ortiz | Xavier Gonze | Félix Antoine Goudreault | Andreas Grüneis | Raveena Gupta | Bogdan Guster | D. R. Hamann | Xu He | Olle Hellman | N. A. W. Holzwarth | F. Jollet | Pierre Kestener | Ioanna-Maria Lygatsika | Olivier Nadeau | Lórien MacEnulty | Enrico Marazzi | Maxime Mignolet | David D. O’Regan | Robinson Outerovitch | Charles Paillard | Guido Petretto | Samuel Poncé | Francesco Ricci | Gian‐Marco Rignanese | Mauricio Rodríguez‐Mayorga | A. Romero | Samare Rostami | Miquel Royo | Marc Sarraute | Alireza Sasani | François Soubiran | Massimiliano Stengel | Christian Tantardini | Marc Torrent | Victor Trinquet | Vasilii Vasilchenko | David Waroquiers | Asier Zabalo | Austin Zadoks | Huazhang Zhang | Josef W. Zwanziger
Source: The Journal of Chemical Physics (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Abinit 2025: New capabilities for the predictive modeling of solids and nanomaterials Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper presents the novel features and capabilities, both technical and scientific, which have been implemented over the past 5 years. This evolution occurred in the context of evolving hardware platforms, high-throughput calculation campaigns, and the growing use of machine learning to predict properties based on databases of first-principle results. We present new methodologies for ground states with constrained charge, spin, or temperature; for density functional perturbation theory extensions to flexoelectricity and polarons; and for excited states in many-body frameworks including GW, dynamical mean field theory, and coupled cluster. Technical advances have extended Abinit high-performance execution to graphical processing units and intensive parallelism. Second-principles methods build effective models on top of first-principle results to scale up in length and time scales. Finally, workflows have been developed in different community frameworks to automate Abinit calculations and enable users to simulate hundreds or thousands of materials in controlled and reproducible conditions. Workflow ; Context (archaeology) ; Software ; Field (mathematics) ; Computation ; Excited state Nonlocal and gradient elasticity in micro/nano structures ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Wenjian Guo | Fayuan Li | Lingyu Wang | Li’an Zhu | Yicong Ye | Zhen Wang | Bin Yang | Shifeng Zhang | Shuxin Bai
Source: Advanced Powder Materials (article, 2025-03-28)
Field: Materials Science — Ceramics and Composites

Accelerated discovery of near-zero ablation ultra-high temperature ceramics via GAN-enhanced directionally constrained active learning In materials science, a significant correlation often exists between material input parameters and their corresponding performance attributes. Nevertheless, the inherent challenges associated with small data obscure these statistical correlations, impeding machine learning models from effectively capturing the underlying patterns, thereby hampering efficient optimization of material properties. This work presents a novel active learning framework that integrates generative adversarial networks (GAN) with a directionally constrained expected absolute improvement (EAI) acquisition function to accelerate the discovery of ultra-high temperature ceramics (UHTCs) using small data. The framework employs GAN for data augmentation, symbolic regression for feature weight derivation, and a self-developed EAI function that incorporates input feature importance weighting to quantify bidirectional deviations from zero ablation rate. Through only two iterations, this framework successfully identified the optimal composition of HfB 2 -3.52SiC-5.23TaSi 2 , which exhibits robust near-zero ablation rates under plasma ablation at 2500 ​°C for 200 ​s, demonstrating superior sampling efficiency compared to conventional active learning approaches. Microstructural analysis reveals that the exceptional performance stems from the formation of a highly viscous HfO 2 -SiO 2 -Ta 2 O 5 -HfSiO 4 -Hf 3 (BO 3 ) 4 oxide layer, which provides effective oxygen barrier protection. This work demonstrates an efficient and universal approach for rapid materials discovery using small data. In this work, a novel active learning framework combining GAN-based data augmentation and symbolic regression, with a developed EAI acquisition function, successfully identified an optimal UHTC formulation (HfB 2 -3.52SiC-5.23TaSi 2 ) in just two iterations. This composition demonstrated robust near-zero ablation rates at 2500 ​°C, highlighting the superior sampling efficiency of the developed framework over traditional active learning. • GAN-enhanced active learning enables rapid discovery of near-zero ablation UHTCs. • Novel EAI function with directional constraints optimizes HfB 2 -based formulations. • Framework shows broad applicability for materials discovery with small data. Materials science ; Ablation ; Ceramic ; Optoelectronics ; Composite material ; Engineering ; Aerospace engineering Advanced ceramic materials synthesis ; Diamond and Carbon-based Materials Research ; Advanced Materials Characterization Techniques
Authors: Qingyue Chen | Weijin Zhang | Xiaocheng Liang | Hao Feng | Weibin Xu | Pengrui Wang | Jian Pan | Benjun Cheng
Source: Materials (article, 2025-03-20)
Field: Materials Science — Ceramics and Composites

Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite–Corundum Ceramics Mullite-corundum ceramics are pivotal in heat transfer pipelines and thermal energy storage systems due to their excellent mechanical properties, thermal stability, and chemical resistance. Establishing relationships and mechanisms through traditional experiments is time-consuming and labor-intensive. In this study, gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN) models were developed to predict essential properties such as apparent porosity, bulk density, water absorption, and flexural strength of mullite-corundum ceramics. The GBR model (<i>R</i><sup>2</sup> 0.91-0.95) outperformed the RF and ANN models (<i>R</i><sup>2</sup> 0.83-0.89 and 0.88-0.91, respectively) in accuracy. Feature importance and partial dependence analyses revealed that sintering temperature and K<sub>2</sub>O (~0.25%) positively affected bulk density while negatively influencing apparent porosity and water absorption. Additionally, sintering temperature, additives, and Fe<sub>2</sub>O<sub>3</sub> (optimal content ~5% and 1%, respectively) were positively related to flexural strength. This approach provided new insight into the relationships between feedstock compositions and sintering process parameters and ceramic properties, and it explored the possible mechanisms involved. Corundum ; Mullite ; Materials science ; Sintering ; Ceramic ; Flexural strength ; Absorption of water ; Porosity ; Composite material ; Bulk density ; Mineralogy ; Environmental science Advanced ceramic materials synthesis ; Recycling and utilization of industrial and municipal waste in materials production ; Iron and Steelmaking Processes