jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. Balaji | Ranjeet Kumar Sahu | S. Narendranath
Source: Smart Materials and Structures (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Process parametric and performance characteristics study of WED machined Ni–Ti–Hf high-temperature shape memory alloys: an experimental and artificial intelligence approach Abstract In recent years, to meet the shortcomings of the conventional machining of Ni–Ti–Hf shape memory alloy (SMA), Wire Electric Discharge Machining (WEDM) as one of the unconventional machining methods, has emerged as the preferred method for processing SMAs. Therefore, in this study, according to the Response Surface Methodology-based Central Composite Design layout, WED machining of Ni–Ti–Hf SMA is carried out using the control parameters like spark time ( S ON ), spark pause time ( S OFF ), gap voltage ( V g ), and dielectric flow rate ( F DL ). A General Regression Neural Network (GRNN) model was used to predict the critical WEDM responses: material removal rate (MRR), surface roughness (Ra), and kerf width (KW). The GRNN model closely agrees with the experimental WEDM responses, resulting in a Mean Absolute Percentage Error below 2%. Field Emission Scanning Electron Microscopy result revealed a recast layer thickness of 10.64 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>±</mml:mo> </mml:mrow> </mml:math> 2.06 µ m and 38.19 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>±</mml:mo> </mml:mrow> </mml:math> 9.55 µ m for the samples with the lowest surface roughness (Ra) and highest MRR, respectively. The shape recovery test result shows a less than 4% reduction in recovery ratio post-WEDM. Further, electrochemical corrosion studies revealed that owing to these surface defects, the corrosion rate increased with higher discharge energy. The corrosion rate of the base material, low Ra sample, and high MRR sample were 0.0013 mm yr −1 , 0.0128 mm yr −1 , and 0.0334 mm yr −1 , respectively. Shape-memory alloy ; Materials science ; Parametric statistics ; Process (computing) ; Metallurgy ; Mechanical engineering ; Computer science ; Engineering ; Mathematics Shape Memory Alloy Transformations
Authors: Taichi Sano (1762624) | Yuki Terauchi (4007291) | Yuki Ide (11199155) | Ichigaku Takigawa (185043) | Tsuyoshi Minami (1430497) | Yasuhide Inokuma (1697392)
Source: Figshare (article, 2025-07-12)
Field: Chemistry — Spectroscopy

Image-BasedMachine Learning Using Inkjet-PrintedChemicals: Mixing Ratio Prediction and Metal Ion Detection Inkjet printing of π-conjugated organic compounds enabled rapid, low-cost generation of training images for the image-based machine learning (ML) prediction of mixing ratios. ML models with mean absolute errors below 4% were achieved within hours, even for dyes with subtle color differences. Changing the printing surface from filter paper to a polypropylene film extended the method to colorless compounds, including isomeric and macrocyclic systems. This approach also enabled spatial mapping of sub-microgram levels of Zn<sup>2+</sup> ions using a weakly responsive colorimetric sensor, without the need for a spectrometer. This work demonstrates a simple, versatile strategy for integrating π-conjugated materials with ML in colorimetric sensing and mixture analysis. Mixing (physics) ; Filter (signal processing) ; Filter paper ; Pattern recognition (psychology) ; Ion chromatography ; Metal ions in aqueous solution ; Mixing ratio ; Absolute (philosophy) ; Analytical Chemistry (journal) Molecular Sensors and Ion Detection ; Advanced Chemical Sensor Technologies ; Luminescence and Fluorescent Materials
Authors: Bin Wang | Xing Duan | Qi Han | Zhe Kong
Source: ChemistrySelect (article, 2025-09-25)
Field: Chemistry — Inorganic Chemistry

Machine Learning‐Driven Prediction and Design of High‐Performance Metal‐Organic Frameworks for CO <sub>2</sub> Capture Abstract Metal‐organic frameworks (MOFs) has been extensively employed in CO 2 capture because of high Brunauer–Emmett–Teller (BET) surface area, large pore volume, modifiability, and designability. The high diversity of metal clusters and organic ligands offers both hope and challenges for the design of novel MOFs with excellent CO 2 adsorption performance. Here we propose an automated nanoporous materials discovery method, powered by machine learning and genetic algorithm, for the generative design of MOFs. We demonstrate the automated design process of MOFs, with the goal of efficiently capturing CO 2 . The method provides a theoretical study for the design of MOFs with the ability to selectively capture CO 2 . Process (computing) ; Co-design ; Nanoporous ; Adsorption ; Generative grammar ; Design process ; Metal-organic framework Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science
Authors: Cory Matsumoto | Xiaoyang Ma | Patrick Truong | Yu Gao
Source: ChemRxiv (preprint, 2025-05-16)
Field: Chemistry — Spectroscopy

Proteome-wide Alkylation to Improve Peptide Sequence Coverage and Detection Sensitivity Effective ionization of peptides is critical for the sensitivity and accuracy of proteomic analyses, especially in nanoproteomics and single-cell proteomics, where the material is scarce. Previous studies have demonstrated the potential of peptide alkylation to improve ionization on certain peptides. In this work, we extend the scope of peptide alkylation by systematically modifying peptides with alkyl chains ranging from one to six carbons, aiming to enhance the ionization signal across a broad peptide spectrum. Our global alkylation approach revealed a significant increase in the ionization signal for a large portion of peptides, underscoring the technique's potential to improve proteomic analyses' sensitivity and overall sequence coverage. By employing machine learning to examine the relationship between alkylation-induced signal enhancement and various peptide characteristics, we found that the increase in signal strength is closely connected to peptide hydrophobicity. This relationship suggests that alkylation can particularly augment the detectability of hydrophilic peptides. Our findings not only highlight the importance of hydrophobicity in peptide ionization efficiency but also suggest that strategic alkylation could be a powerful tool to overcome the limitations imposed by sample size in proteomic with limited sample size. Sensitivity (control systems) ; Alkylation ; Proteome ; Peptide ; Sequence (biology) ; Chemistry ; Computational biology ; Combinatorial chemistry ; Computer science ; Biology ; Biochemistry ; Engineering Advanced Proteomics Techniques and Applications ; Chemical Synthesis and Analysis ; Machine Learning in Bioinformatics
Authors: Hatem  Abdul Kareem Nori
Source: Al Rafidain Journal of Engineering Sciences (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Modeling and Simulation of Flexural Strength in Epoxy Graphene CNT Hybrids Using Python Tools This paper provides a highly detailed experimental and predictive exploration of how to enhance flexural strength of polymer composites with the technique of bi-reinforced graphene nanoplatelets (GNP) and carbon nanotubes (CNT). One hundred and fifty hybrid combinations based on varying GNP (0-1 wt.%) and CNT (0-0.4 wt.%) contents were developed and prepared accordingly to determine the effects of hybrid fillers in flexural performance. Experimental findings showed that the un-reinforced baseline sample (0% GNP, 0% CNT) had flexural strength 68.90 MPa, whereas the strongest sample was recorded with 1% GNP and 0.3% CNT (120.20 MPa) which is an increment of 74.44 % as compared to the un-reinforced sample. Simultaneously, a regression model in the form of machine learning was trained to estimate the values of the flexural strength based on the filler contents as the input variables. The model proved very reliable with the highest strength being predicted as 100.85 MPa at the same optimum hybrid proportion, and lowest values of absolute and percent error in all specimens tested. The results of the model produced reasonably accurate results as compared to experimental data having R 2 values greater than 0.96, and an exclusively overlaying of predicted points on the 95 percent confidence interval. Flexural strength ; Epoxy ; Python (programming language) ; Graphene ; Carbon nanotube Carbon Nanotubes in Composites ; Graphene research and applications ; Polymer Nanocomposites and Properties
Authors: M. Isoz | A. Kovárnová | Petra Gruber | L. Kubíčková | Damien Gabriel | Jan Kaufman | Jan Brajer
Source: Engineering Mechanics ... (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

On Model Order Reduction in Simulations of Laser Shock Peening Laser shock peening (LSP) is a modern but already established approach to improve the strength and fatigue resistance of components by hardening their surface.LSP is based on the use of a high-energy laser to generate mechanical loading of the component surface.Compared to standard peening techniques, e.g., shot peening, LSP allows for precision surface treatment since the individual laser spots can be positioned with an accuracy of the order of 0.1 mm.Furthermore, the laser energy ( 1 J) as well as the spot size ( 1 mm) are tunable parameters.This process tunability allows for component-and material-based optimization that can be accelerated through simulations.However, simulations of LSP treatment of real-life components are time consuming in such a way that it disallows their direct usage in process optimization.In this contribution, we present an approach to mitigate the computational costs of simulation-based LSP process optimization.The approach is based on sampling the optimization parameter space by computing several simulations while varying the optimized parameters.Next, the precomputed solutions are used to construct a reduced order model (ROM) usable in optimization.ROM in question leverages proper orthogonal decomposition (POD) and artificial neural networks (ANNs).Compared to the original full order model (FOM), ROM evaluation is by orders of magnitude faster.Furthermore, the loss of information between FOM and ROM can be controlled during the ROM construction. Peening ; Reduction (mathematics) ; Shock (circulatory) ; Materials science ; Electric shock ; Laser ; Computer science ; Mechanics ; Mechanical engineering ; Optics ; Engineering ; Physics ; Mathematics ; Medicine ; Geometry High-Velocity Impact and Material Behavior ; Surface Treatment and Residual Stress ; Laser-Plasma Interactions and Diagnostics
Authors: Hao Wu | Yanjie Zhu
Source:  (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

HG-KAN: A Hybrid Gated Kolmogorov-Arnold Network for Automated CEST Signal Extraction Chemical Exchange Saturation Transfer (CEST) is a powerful magnetic resonance imaging (MRI) technique that indirectly probes solute exchange-related signals via water, providing valuable metabolic information. In signal extraction, multi-pool Lorentzian fitting (MPLF) is the established traditional solution; however, its acute sensitivity to initial parameters necessitates a tedious, subjective, and expertise-dependent manual tuning process, representing a primary bottleneck to its clinical translation. To address this challenge, we introduce the Hybrid Gated Kolmogorov-Arnold Network (HG-KAN), a novel deep learning framework for direct, non-iterative signal extraction. The HG-KAN architecture is specifically engineered to deconstruct the Z-spectrum's composite signal. It leverages wavelet basis functions to precisely represent the sharp, localized resonant peaks characteristic of CEST effects. Concurrently, it employs B-spline basis functions to efficiently model the broad, smoothly varying baseline components. We demonstrate that our framework successfully automates the quantification process, eliminating the need for manual parameter tuning while achieving accuracy comparable to the MPLF gold-standard and state-of-the-art performance among competing deep learning approaches. By replacing the complex, user-dependent iterative fitting procedure with a direct and robust automated framework, HG-KAN removes a critical barrier to the inefficiency and wider clinical adoption of quantitative CEST. SIGNAL (programming language) ; Sensitivity (control systems) ; Bottleneck ; Artificial neural network ; Deep learning ; Pattern recognition (psychology) ; Signal processing ; Basis (linear algebra) Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Functional Brain Connectivity Studies
Authors: Khaled SharafEldin | Bryan D. Miller | Wenjun Liu | J. Z. Tischler | Benjamin Anglin | Anter El‐Azab
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Informed Unsupervised Machine Learning Analysis of Dislocation Microstructure from High-Resolution Differential Aperture X-Ray Structural Microscopy Data Dislocation ; Microscopy ; Materials science ; Resolution (logic) ; Differential (mechanical device) ; Microstructure ; Optics ; X-ray ; Crystallography ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Physics ; Chemistry ; Composite material Microstructure and mechanical properties ; Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
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.org (article, 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: Amélie Descamps (9012299) | Hannah Hirmz (17587124) | Ewerton de Oliveira (21558198) | Anton De Spiegeleer (518360) | Juliana Feio (21558201) | Kaue da Costa (21558204) | Bart De Spiegeleer (8260641) | Evelien Wynendaele (443950)
Source: Figshare (article, 2025-06-17)
Field: Chemistry — Spectroscopy

Beyond Molecular Weight: Peptide Characteristics Influencingthe Sensitivity of Retention to Changes in Organic Solvent in Reversed-PhaseChromatography The <i>S</i> value, representing the sensitivity of peptide retention to changes in organic solvent concentrations in reversed-phase liquid chromatography (RP-LC), is a critical parameter for optimizing gradient elution and enhancing separation selectivity. While the influence of molecular weight on <i>S</i> values is well-documented, the contribution of other peptide-specific characteristics remains insufficiently understood. This study experimentally evaluated a structurally diverse dataset of 18 peptides and applied a multifaceted data approach combining partial least squares, Kendall’s tau correlation analysis, and machine learning models (multiple linear regression, decision tree, and lasso) to identify key molecular features affecting the <i>S</i> value. Factors such as hydrophobicity and the number of CH<sub>2</sub>R<sub>2</sub> groups, structural flexibility, isoelectric point, and the number of lysine residues emerged as significant contributors. A predictive model was developed, offering a tool for method optimization in peptide separations. Peptide ; Organic solvent ; Sensitivity (control systems) ; Elution ; Solvent ; Isoelectric focusing ; Lysine ; Isoelectric point Analytical Chemistry and Chromatography ; Protein purification and stability ; Chromatography in Natural Products
Authors: Jing Ping (1468486) | Koki Muraoka (2632864) | Zhendong Liu (1235259) | Akira Nakayama (1353762)
Source: Figshare (article, 2025-10-31)
Field: Chemistry — Inorganic Chemistry

Unveiling theStructural Factors Governing the Diffusionof Ethene in Small-Pore Zeolites through Machine Learning Investigating diffusion dynamics in nanoscale confined spaces of zeolites has long been of significant interest. Although zeolite structures are known to significantly influence their diffusion properties, determining how a specific structure impacts diffusion performance is challenging. This difficulty arises from a lack of methodologies for quantifying structural factors and linking correlated variables to the diffusion performance. To address this challenge, this study utilizes a data-driven approach to systematically assess diffusion behaviors in small-pore zeolites. Machine learning models were trained and achieved high diffusion predictive performance through newly devised descriptors. Model interpretation and further molecular dynamics simulations highlighted the significant role of the cage architecture in influencing diffusion. Moreover, we discovered that small-pore zeolites with specific stacking sequences, the parallel stacking of 6-rings, acquired enhanced diffusion properties synergistically as the proportion of double-six-ring units increased. This study advances the understanding of diffusion in micropores and offers insights into zeolite design for promoting molecular transport. Stacking ; Diffusion ; Zeolite ; Interpretation (philosophy) ; Molecular dynamics ; Nanoscopic scale ; Diffusion process Zeolite Catalysis and Synthesis ; Nanopore and Nanochannel Transport Studies ; Catalysis and Oxidation Reactions
Authors: Ping Jing | Koki Muraoka | Zhendong Liu | Akira Nakayama
Source: The Journal of Physical Chemistry Letters (article, 2025-10-31)
Field: Chemistry — Inorganic Chemistry

Unveiling the Structural Factors Governing the Diffusion of Ethene in Small-Pore Zeolites through Machine Learning Investigating diffusion dynamics in nanoscale confined spaces of zeolites has long been of significant interest. Although zeolite structures are known to significantly influence their diffusion properties, determining how a specific structure impacts diffusion performance is challenging. This difficulty arises from a lack of methodologies for quantifying structural factors and linking correlated variables to the diffusion performance. To address this challenge, this study utilizes a data-driven approach to systematically assess diffusion behaviors in small-pore zeolites. Machine learning models were trained and achieved high diffusion predictive performance through newly devised descriptors. Model interpretation and further molecular dynamics simulations highlighted the significant role of the cage architecture in influencing diffusion. Moreover, we discovered that small-pore zeolites with specific stacking sequences, the parallel stacking of 6-rings, acquired enhanced diffusion properties synergistically as the proportion of double-six-ring units increased. This study advances the understanding of diffusion in micropores and offers insights into zeolite design for promoting molecular transport. Stacking ; Diffusion ; Zeolite ; Interpretation (philosophy) ; Molecular dynamics ; Nanoscopic scale ; Diffusion process Zeolite Catalysis and Synthesis ; Nanopore and Nanochannel Transport Studies ; Catalysis and Oxidation Reactions
Authors: Nelson, John P. | Olugbade, Olajide | Shapira, Philip | Biddle, Justin B.
Source: arXiv (Cornell University) (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

Can Artificial Intelligence Accelerate Technological Progress? Researchers' Perspectives on AI in Manufacturing and Materials Science Artificial intelligence (AI) raises expectations of substantial increases in rates of technological and scientific progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes. Accordingly, it remains unclear how and to what extent AI can accelerate innovation. To help to fill this gap, we report results from 32 interviews with U.S.-based academic manufacturing and materials sciences researchers experienced with AI and machine learning (ML) techniques. Interviewees primarily used AI for modeling of materials and manufacturing processes, facilitating cheaper and more rapid search of design spaces for materials and manufacturing processes alike. They report benefits including cost, time, and computation savings in technology development. However, interviewees also report that AI/ML tools are unreliable outside design spaces for which dense data are already available; that they require skilled and judicious application in tandem with older research techniques; and that AI/ML tools may detrimentally circumvent opportunities for disruptive theoretical advancement. Based on these results, we suggest there is reason for optimism about acceleration in sustaining innovations through the use of to AI/ML; but that support for conventional empirical, computational, and theoretical research is required to maintain the likelihood of further major advances in manufacturing and materials science. Big data ; Applications of artificial intelligence ; Manufacturing ; Technological change ; Disruptive technology ; Optimism Machine Learning in Materials Science ; Digital Transformation in Industry ; Artificial Intelligence in Healthcare and Education