jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manyu Xiao | Wenqing Zheng | Xingang Luan | Xinming Xu
Source: Springer Link (Chiba Institute of Technology) (article, 2025-05-26)
Field: Materials Science — Ceramics and Composites

Data-driven Oxidation Kinetic Model Construction of Silicon Carbide Composites Silicon carbide fiber-reinforced silicon carbide matrix composites (SiCf/SiC) exhibit exceptional properties, including lightweight, high strength, superior heat resistance, and high fracture toughness. However, the material's inherent inhomogeneity, nonlinearity, anisotropy, and diverse failure modes make the damage and failure mechanisms of SiCf/SiC composites highly complex. This research integrates experimental methods with intelligent data analysis techniques to accurately characterize and predict the oxidation behavior and mechanical property evolution of SiCf/SiC composites under complex service conditions. By leveraging mechanical performance evolution data of SiCf/SiC in various single environments, along with Gaussian functions and parameter optimization methods, and incorporating the composite material's chemical reaction characteristics, an automatic construction of the oxidation kinetics model for SiCf/SiC under complex multi-field coupling environments was achieved based on data-driven strategy. The constructed SiCf/SiC oxidation kinetics model precisely delineates the relationship between the mechanical performance parameters after high-temperature oxidation in different environments and their service time. Furthermore, a complex coupling relationship model was developed that links the mechanical performance parameters with service time and service temperature. Experimental verification confirmed the accuracy and effectiveness of the constructed oxidation kinetics model in predicting the performance evolution of SiCf/SiC composites under various environmental conditions. Silicon carbide ; Kinetics ; Composite number ; Coupling (piping) ; Carbide ; Ceramic matrix composite ; Service life Advanced ceramic materials synthesis ; MXene and MAX Phase Materials ; Silicon Carbide Semiconductor Technologies
Authors: Yunqi Li | Qiang Ao | Chunhui Xie | Yang You | Haibo Xie | Mengmeng Qin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Data-driven models to explore regulatable variables for the density, thermal conductivity and compressive strength of rigid polyurethane foams Thermal conductivity ; Compressive strength ; Robustness (evolution) ; Curing (chemistry) ; Polyurethane ; Conductivity ; Thermal ; Measure (data warehouse) Polymer composites and self-healing ; Polymer Foaming and Composites ; Thermal properties of materials
Authors: Yingji Wang (16624716) | Jin Sun (117866) | Liping Zhou (814435) | Guangrun Wu (18987554) | Siqi Gong (436793) | Zibo Gao (8315094) | Jing Wu (54032) | Chaochao Ma (11825525) | Yun Zou (165943) | Xiaoyu Liu (206872) | Rongheng Ma (20666846) | Xin Zhang (35492) | Zhaoying Zhang (2657998) | Yang Li (7082)
Source: Figshare (article, 2025-02-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Highly Sensitive and Interference-Free Detection ofMultiple Drug Molecules in Serum Using Dual-Modified SERS SubstratesCombined with AI Algorithm Analysis Surface-enhanced Raman spectroscopy (SERS) technology has shown broad potential in drug concentration detection, but its application in blood drug monitoring faces significant challenges. The primary difficulty lies in overcoming the interference caused by various biomolecules present in serum, which can severely obscure the SERS signals of target drug molecules. Traditional enhancement substrates are often limited to detecting single drugs and are prone to interference, making the label-free detection of multiple drugs particularly challenging. To address these issues, we developed a novel SERS substrate based on Au@AgNRs, which undergoes a two-step modification to produce Au@AgDBCNRs. This innovative substrate provides exceptional signal amplification, simultaneously allowing the sensitive detection of multiple drug molecules. Moreover, our method eliminates the need for serum deproteinization, enabling the direct detection of drugs in serum while effectively mitigating interference from blood components. The cetyltrimethylammonium bromide coating on Au@AgDBCNRs is an internal standard for drug quantification without additional standards. The platform significantly improves detection accuracy and efficiency by automatically integrating artificial intelligence to recognize and analyze Raman spectral features. This novel SERS platform provides a new idea for therapeutic drug monitoring and is expected to provide rapid, accurate, and cost-effective drug detection in the clinical environment, which has great potential in improving patient care and optimizing drug dosage strategies. Drug ; Drug detection ; Biomolecule ; Interference (communication) ; Therapeutic drug monitoring ; Substrate (aquarium) ; Drug discovery Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Helen Rutlidge | Russell Pickford | Tina Ventura | Rita K. Henderson
Source:  (preprint, 2025-03-15)
Field: Chemistry — Spectroscopy

Strategies to deal with batch effects with high resolution Orbitrap mass spectrometry for NOM characterisation There is growing evidence that changes in the molecular composition of natural organic matter (NOM) in water drives changes in the effectiveness of water treatment processes. Hence, there is a growing interest in obtaining more detailed characterisation of natural organic matter, than traditional methods can provide. High resolution mass spectrometry is one such technique that is increasingly being used for NOM analysis. Predominately, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), has been used but Orbitrap MS is emerging as a more available, smaller, and cheaper alternative. &amp;#160;Due to the high sensitivity of high-resolution mass spectrometry instrumental performance variation from day to day is a recognised problem. This means that samples measured on different days may not be directly comparable, likely due to slight variations in equipment operating conditions, laboratory ambient conditions and minor contamination left from other analysis. The aim of this study was to investigate the impact of this instrumental variation using a NOM standard mixture and ways to overcome it.To understand the Orbitrap MS instrumental variation from day to day, a freshly prepared NOM standard mixture was analysed on several days. The data files were compiled and analysed using Compound Discoverer software. The molecular weights observed were assigned to molecular formula using the software. As part of the data processing various strategies were explored to deal with batch effects, including data-driven normalisation, removal of data with lower relative abundance and Systematic Error Removal Using Random Forest (SERRF).For the NOM standard mixture, there were differences in the assigned molecular formulas as well as the relevant abundances. A total of 940 molecular formula were found for all the NOM mixture standard runs, with 357 found in more than one sample run. However, the compounds that were present in only one or two sample runs tended to have lower relative abundance, and hence removing compounds with lower relative abundance may reduce the influence of instrumental variation. In general, the greatest commonality across the sample runs was seen in the region where the H/C ratio was between 0.5-1.5 and the O/C ratio was Orbitrap ; Mass spectrometry ; Chemistry ; DART ion source ; High resolution ; Chromatography ; Remote sensing ; Geography ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Water Quality Monitoring and Analysis
Authors: Li-sha Rong | Shaobing Yang | Li Li | Yuanzhong Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Origin Traceability of Dendrobium Officinale from Yunnan Using Near- and Mid-Infrared Spectroscopy Combined with Dd-Simca Modeling Traceability ; Dendrobium ; Computer science ; Traditional medicine ; Medicine Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Essential Oils and Antimicrobial Activity
Authors: 鑫 张
Source: Journal of Advances in Physical Chemistry (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Research Progress and Prospect of Porphyrin-Based Organic Framework Materials and Their Applications in Sensing Detection 卟啉基金属有机框架材料(PMOFs)相比于传统传感材料(如酶、抗体)具有更高的比表面积、优异的光电活性、可调孔径及良好的生物相容性，在传感检测领域展现出了独特优势。本文系统综述了PMOFs的关于金属中心调控、卟啉功能化修饰、混合配体设计策略以及后合成修饰、复合功能化等功能化方法。探讨了PMOFs目前在荧光、比色、电化学及光电化学等传感检测中的应用进展。概括描述了PMOFs在未来发展的方向。譬如其目前在稳定性、规模化制备等方面仍面临挑战，在未来发展方向应往多模态传感融合、智能响应材料开发及结合人工智能技术来发展。为PMOFs的实际传感检测应用提供了理论支持和技术参考。Porphyrin-based organic framework materials (PMOFs) have higher specific surface area, excellent photoelectric activity, adjustable pore size and good biocompatibility compared with traditional sensing materials (such as enzymes and antibodies), which have shown unique advantages in the field of sensing and detection. In this paper, the functionalization methods of PMOFs, such as metal center regulation, porphyrin functionalization, hybrid ligand design strategies, post-synthetic modification, and complex functionalization, are systematically reviewed. The current application progress of PMOFs in fluorescence, colorimetric, electrochemical and photoelectrochemical sensing detection is discussed. The direction of PMOFs development in the future is summarized. For example, it still faces challenges in stability and large-scale preparation. In the future, the development direction should be developed towards multimodal sensor fusion, intelligent response material development, and combination with artificial intelligence technology. This study provides theoretical support and technical reference for the practical sensing application of PMOFs. Porphyrin ; Nanotechnology ; Environmental science ; Materials science ; Engineering physics ; Systems engineering ; Remote sensing ; Astrobiology ; Biochemical engineering ; Chemistry ; Engineering ; Geology ; Physics ; Photochemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Molecular Sensors and Ion Detection
Authors: Amelia Carolina Sparavigna
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Spettroscopia Raman di Materiali Carbonacei con AI: Deconvoluzione Spettrale, Analisi e Caratterizzazione Strutturale (Raman Spectroscopy of Carbonaceous Materials with AI: Spectral Deconvolution, Analysis, and Structural Characterization) Raman spectroscopy ; Characterization (materials science) ; Materials science ; Deconvolution ; Physics ; Nanotechnology ; Optics Spectroscopy and Chemometric Analyses ; Gas Sensing Nanomaterials and Sensors ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Walter Cañón‐Mancisidor | Christopher González | Lorena E. Rosaleny | Alejandro Gaita‐Ariño
Source: Dalton Transactions (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SIMDAVIS 1.2: phosphonates are outstanding SIM ligands. Crown ethers are not This study presents SIMDAVIS 1.2, an updated dataset and analysis tool for lanthanide-based single-ion magnets (Ln-SIMs), focusing on the role of various ligands in influencing their magnetic properties. The revised dataset, accessible <i>via</i> a user-friendly online dashboard, encompasses over 10 000 data points on chemical and physical properties derived from more than 400 publications. Notably, the study redefines ligand classifications, introducing phosphonate-based ligands as a distinct family and highlighting their superior performance as SIM ligands compared to less effective alternatives such as crown ethers. Phosphonates exhibit promising pentagonal bipyramidal geometries that enhance magnetic stability by minimizing low-energy vibrational interactions. Comparative analysis reveals that while phosphonates may have moderate <i>U</i><sub>eff</sub> values compared to other leading ligands, their magnetic memory and hysteresis properties are comparable to those of bis-phthalocyaninato or metallocene sandwiches. This work emphasizes the importance of rational ligand selection and frugal data-driven approaches in the design of effective Ln-SIMs, contributing valuable insights into the optimization of single-ion magnet design strategies. Crown (dentistry) ; Chemistry ; Stereochemistry ; Materials science ; Composite material Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Polyoxometalates: Synthesis and Applications
Authors: Xuezhang Xiao | Marco F. P. ten Eikelder | Tianyue Yang | Yiqing Li | Kan He | Shuo Wang | Peter V. Coveney
Source: ArXiv.org (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model Phase separation in binary mixtures, governed by the Cahn-Hilliard equation, plays a central role in interfacial dynamics across materials science and soft matter. While numerical solvers are accurate, they are often computationally expensive and lack flexibility across varying initial conditions and geometries. Neural operators provide a data-driven alternative by learning solution operators between function spaces, but current architectures often fail to capture multiscale behavior and neglect underlying physical symmetries. Here we show that an equivariant U-shaped neural operator (E-UNO) can learn the evolution of the phase-field variable from short histories of past dynamics, achieving accurate predictions across space and time. The model combines global spectral convolution with a multi-resolution U-shaped architecture and regulates translation equivariance to align with the underlying physics. E-UNO outperforms standard Fourier neural operator and U-shaped neural operator baselines, particularly on fine-scale and high-frequency structures. By encoding symmetry and scale hierarchy, the model generalizes better, requires less training data, and yields physically consistent dynamics. This establishes E-UNO as an efficient surrogate for complex phase-field systems. Equivariant map ; Convolution (computer science) ; Operator (biology) ; Artificial neural network ; Binary number ; Flexibility (engineering) ; Function (biology) ; Scale (ratio) ; Symmetry (geometry) ; Translation (biology) Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Saad Lahrichi | Eekhout Mick | Marshall B. Lindsay | Scott D. Kovaleski | Derek T. Anderson | Jordan M. Malof | Stanton R. Price | Steven R. Price
Source:  (article, 2025-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep inverse modeling the near field response of optical metasurfaces In this work, we investigate the problem of real-time design of electromagnetic (EM) metamaterials to achieve custom scattering properties: a type of inverse modeling problem. To address this problem, we investigate a class of DNN-based models that are specially designed to address inverse problems, termed deep inverse models (DIMs). DIMs have recently shown tremendous promise for solving material design problems, however, relatively less work has been done for high-dimensional problems, such as near-field design. In this work, we performed 1500 simulations of a metasurface with a 3x3 array of meta-atom pillars, where we independently and randomly-varied the radii of each pillar and recorded the resulting electric near-field values. We then used this dataset to train and evaluate several data-driven inverse models, including several variations of a recently-successful DIM, termed the Tandem. Our results indicate that the Tandem is capable of making relatively accurate design predictions in this challenging high-dimensional settings, and doing so in real-time (e.g., roughly 4ms). We find that the choice of model architecture significantly impacts the accuracy of the inverse model, and even higher accuracy can be achieved with further improvements to the Tandem’s design. Inverse ; Computer science ; Field (mathematics) ; Inverse problem ; Optical imaging ; Optics ; Physics ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Optimization
Authors: Yitao Shen | Zheyu Wang | Sen Chen | Xingcai Lü | Beibei Han | Boyuan Wang | Xiao Ma | Shijin Shuai
Source: Journal of Power Sources (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Prognostication of solid oxide fuel cells performance under agglomeration of particles and oxidation of nickel Overpotential ; Economies of agglomeration ; Anode ; Solid oxide fuel cell ; Oxide ; Degradation (telecommunications) ; Software deployment ; Nickel ; Work (physics) Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: B. Kieskamp | Amir Houshang Mahmoudi | Mina Shahi
Source: Results in Engineering (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

A framework for predictive kinetic modeling of materials for thermochemical energy storage using algorithmic optimization The effective design and optimization of Thermochemical Energy Storage (TCES) systems are often hindered by the lack of predictive kinetic models for materials with complex, multi-step reaction behavior. This study addresses this challenge for sodium sulfide ( ), a promising TCES material whose multi-step reaction pathways have complicated kinetic characterization. A robust and accessible framework is presented, using global optimization algorithms to directly calibrate reaction models from standard thermal analysis data. Hysteresis observed in the equilibrium properties of is first quantified through Simultaneous Thermal Analysis (STA). Subsequently, eight variations of reaction kinetic models are formulated and calibrated using the Shuffled Complex Evolution (SCE) algorithm on time-series STA data. The resulting models are validated, with the best-performing model enabling accurate prediction of reaction rates under varying operating conditions. Predictive accuracy is reduced by a factor of 16.1 outside the calibration temperature range for the best-performing model, highlighting the value of a framework for rapid, application-specific model generation. Sensitivity analysis reveals that model performance is most dependent on activation energy and equilibrium conditions, with an average absolute sensitivity index of 38.6 and 12.4, respectively. Through this novel integration of algorithmic optimization with standard thermal analysis, this study provides a streamlined framework for developing kinetic models, addressing a critical gap that has limited the effective design of TCES systems. • A framework for rapid multi-step TCM characterization is provided. • Na2S kinetics are established, accounting for reaction hysteresis. • Model performance is most sensitive to activation energy and equilibrium conditions. • Limited predictive range reinforces the need for rapid, data-driven modeling. Sensitivity (control systems) ; Kinetic energy ; Range (aeronautics) ; Calibration ; Sodium sulfide ; Model predictive control ; Hysteresis ; Thermal Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Adsorption and Cooling Systems
Authors: Yong Tang | Hao Mo | Qiuxia Li | Wenrou Yu | Zhaoxian Chen | Hailiang Xiong | Yingzhou Huang
Source: Microchemical Journal (article, 2025-09-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cu-Ag micro-nanoparticle for spliced SERS detection of urine and saliva Substrate (aquarium) ; Saliva ; Urine ; Detection limit ; Raman spectroscopy ; Sensitivity (control systems) Gold and Silver Nanoparticles Synthesis and Applications ; Retinal and Optic Conditions ; Biosensors and Analytical Detection
Authors: Tetsuo Asakura | Asuna Hagiya | Takashi Kataoka | Keiji Komatsu | Keisuke Sato
Source: Journal of Magnetism and Magnetic Materials (article, 2025-10-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and characterization of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si11.svg"> <mml:msub> <mml:mtext>LaCo</mml:mtext> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>−</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> <mml:msub> <mml:mi mathvariant="normal">Y</mml:mi> <mml:mi>x</mml:mi> </mml:msub> <mml:msub> <mml:mi mathvariant="normal">O</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:math> by machine learning A physics-informed Gaussian Process Regression (GPR) model with monotonic baseline captured the decreasing trend of the magnetic excitation energy Δ 1 upon Y substitution with quantified uncertainty. Under 3-fold cross validation, the model achieved test RMSE = 7.21 ± 1.18 K and R 2 = 0.907 ± 0.049 (aggregate across all test points: RMSE = 7.28 K , R 2 = 0.935 ), while underestimating Δ 1 for LaCoO 3 (predicting 132 K vs. experimental 160 K ). At x = 0.05 , the model predicted Δ 1 = − 48.7 ± 7.5 K , indicating a transition toward a magnetic ground state; experimentally, the low characteristic energy ( ∼ 45 K ) is consistent with this trend. Uncertainty-aware screening (UCB, κ = 1.0 ) highlighted Y as a practical top candidate among feasible B-site trivalent substituents. • Physics-informed Gaussian process predicts Δ I trends with uncertainty. • Uncertainty-aware screening identifies Y 3+ as non-intuitive B-site dopant. • Y substitution reduces Δ I from 160 K to 45 K in LaCo 1 − x Y x O 3 . • Anisotropic lattice distortion ( c / a +0.43 %, V ±0.3 %) weakens crystal field. • Data-driven approach identifies rare-earth as viable B-site substituent. Characterization (materials science) ; Anisotropy ; Gaussian ; Energy (signal processing) ; Kriging ; Monotonic function ; Substitution (logic) ; Lattice (music) ; Regression Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Xianquan Yan | Hakan Akgün | Kenji Kawaguchi | N. Duane Loh | Ching Hua Lee
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

HSG-12M: A Large-Scale Spatial Multigraph Dataset Existing graph benchmarks assume non-spatial, simple edges, collapsing physically distinct paths into a single link. We introduce HSG-12M, the first large-scale dataset of $\textbf{spatial multigraphs}-$graphs embedded in a metric space where multiple geometrically distinct trajectories between two nodes are retained as separate edges. HSG-12M contains 11.6 million static and 5.1 million dynamic $\textit{Hamiltonian spectral graphs}$ across 1401 characteristic-polynomial classes, derived from 177 TB of spectral potential data. Each graph encodes the full geometry of a 1-D crystal's energy spectrum on the complex plane, producing diverse, physics-grounded topologies that transcend conventional node-coordinate datasets. To enable future extensions, we release $\texttt{Poly2Graph}$: a high-performance, open-source pipeline that maps arbitrary 1-D crystal Hamiltonians to spectral graphs. Benchmarks with popular GNNs expose new challenges in learning from multi-edge geometry at scale. Beyond its practical utility, we show that spectral graphs serve as universal topological fingerprints of polynomials, vectors, and matrices, forging a new algebra-to-graph link. HSG-12M lays the groundwork for geometry-aware graph learning and new opportunities of data-driven scientific discovery in condensed matter physics and beyond. Multigraph ; Graph ; Metric (unit) ; Topology (electrical circuits) ; Pipeline (software) ; Network topology ; Metric space ; Hyperbolic geometry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topological and Geometric Data Analysis