jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Majid Sohrabi | Milad Razbin
Source: Scientific Reports (article, 2025-07-26)
Field: Materials Science — Biomaterials

Hybrid modeling for optimizing electrospun polyurethane nanofibrous membranes in air filtration applications Nanofibers have gained recognition as promising materials for air filtration due to their high surface area-to-volume ratio, adjustable porosity, and exceptional mechanical properties. However, optimizing their structural characteristics to maximize filtration efficiency while minimizing pressure drop remains challenging due to the complexity of the electrospinning process. This study presents an artificial intelligence-based methodology to establish relationships between electrospinning parameters, nanofiber morphology, and filtration performance. An advanced statistical approach is used to systematically collect and analyze data, followed by modeling these relationships using artificial neural networks (ANN) and analytical formulas to enhance predictive accuracy. A genetic algorithm (GA) is subsequently utilized to refine electrospinning parameters, facilitating the production of nanofibers with enhanced filtration efficiency and optimized airflow resistance. The optimized nanofiber membranes are validated experimentally to assess their real-world performance. The findings demonstrate the potential of AI-driven design in fine-tuning nanofiber structures for advanced air filtration applications. The optimized sample achieved a filtration efficiency of 96%, a pressure drop of 110.23 Pa, and a quality factor of 0.0297. This study underscores the effectiveness of combining AI with electrospinning to develop high-performance air filtration materials. Electrospinning ; Polyurethane ; Filtration (mathematics) ; Membrane ; Air filtration ; Materials science ; Nanofiber ; Composite material ; Computer science ; Chemistry ; Polymer ; Particulates ; Mathematics Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Advanced Sensor and Energy Harvesting Materials
Authors: David A. Broadway | Mykhailo Flaks | Adrien E. E. Dubois | Patrick Maletinsky
Source: Physical Review Applied (article, 2025-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reconstruction of nontrivial magnetization textures from magnetic field images using neural networks Spatial imaging of magnetic stray fields from magnetic materials is a useful tool for identifying a material's underlying magnetic configurations; however, transforming a magnetic image into a magnetization image is an ill-posed problem, and this can result in artefacts that limit the inferences that can be drawn from the material under investigation. In this work, we develop a neural-network fitting approach that approximates this transformation, reducing these artefacts. In addition, we demonstrate that the approach allows the inclusion of additional models and bounds that are not possible with traditional reconstruction methods. These advantages allow for the reconstruction of nontrivial magnetization textures with varying magnetization directions in thin-film magnets, which was not possible previously. We demonstrate this capability by performing magnetization reconstructions on a variety of topological spin textures. Magnetization ; Artificial neural network ; Magnetic field ; Condensed matter physics ; Physics ; Computer science ; Artificial intelligence ; Nuclear magnetic resonance ; Quantum mechanics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Neural Networks and Applications
Authors: Eric L. Margelefsky | Benjamin C. Dobson | Tao Chen | Nelson Lee Afanador
Source: Organic Process Research & Development (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Predicting Shock Sensitivity from Differential Scanning Calorimetry Data and Molecular Structure: Beyond the Yoshida Correlation The Yoshida correlation is widely used in the pharmaceutical and fine chemical industry to predict explosivity and shock sensitivity of chemical substances based on the initiation temperature and enthalpy of differential scanning calorimetry (DSC) exotherms. We investigate the origins and accuracy of this correlation (and commonly used modifications thereof) by applying it to a large data set of 383 compounds, which are relevant to the pharmaceutical industry, and demonstrate that the initiation temperature and enthalpy variables are not good predictors for shock sensitivity. By incorporating structural information (for the 292 compounds where it was available), we used machine learning to inform and guide a logistic regression technique to develop a shock sensitivity model which has a higher overall accuracy (63%) and a higher accuracy for shock-sensitive compounds (97%) compared to the original Yoshida correlation (52% overall accuracy, 82% accuracy for shock-sensitive compounds). This logistic regression model includes both the original Yoshida variables (DSC initiation temperature and enthalpy) and also incorporates the oxygen balance (OB100) and the number of energetic nitrogen groups in the molecule. Differential scanning calorimetry ; Enthalpy ; Sensitivity (control systems) ; Thermodynamics ; Shock (circulatory) ; Logistic regression ; Chemistry ; Correlation ; Calorimetry ; Biological system ; Materials science ; Statistics ; Mathematics ; Physics ; Internal medicine ; Medicine Thermal and Kinetic Analysis ; Computational Drug Discovery Methods ; Cholinesterase and Neurodegenerative Diseases
Authors: Jaebum Noh | Jaekyung Kim | Junsuk Rho
Source: Nano Letters (article, 2025-07-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Overcoming Information Sparsity in Metasurfaces for Full-Color Holography via End-to-End Design We propose an end-to-end (E2E) system for RGB meta-hologram generation that efficiently determines the optimal material and geometry for target holograms, eliminating the need for exhaustive simulations of every possible meta-atom configuration. A neural network is developed to accurately map the material and structural parameters to the transmission spectra of the single-layered metasurfaces. The E2E system leverages the network to identify suitable meta-atom candidates, aiming to (i) enhance the hologram efficiency and (ii) minimize cross-talk between images at different channels. By effectively navigating the sparse RGB phase distribution, the system enables the fabrication of an optimized metasurface that projects three distinct images at different wavelengths. We anticipate that our method will contribute to the development of multiplexed holographic displays and also pave the way for integrated metasurface design and optimization of metasurfaces. End-to-end principle ; Holography ; Computer science ; Materials science ; Nanotechnology ; Optoelectronics ; Optics ; Physics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Optical Imaging Technologies
Authors: Liben Guo | Yuanbin Liu | Lei Yang | Bing Cao
Source: Journal of Applied Physics (article, 2025-02-24)
Field: Materials Science — Materials Chemistry

Lattice dynamics modeling of thermal transport in solids using machine-learned atomic cluster expansion potentials: A tutorial Lattice dynamics (LD) plays a crucial role in investigating thermal transport in terms of not only underlying physics but also novel properties and phenomena. Recently, machine learning interatomic potentials (MLIPs) have emerged as powerful tools in computational physics and chemistry, showing great potential in providing reliable predictions of thermal transport properties with high efficiency. This tutorial provides a comprehensive guideline for MLIPs’ development and how they are used for the computational modeling of thermal transport. Using atomic cluster expansion (ACE) as the paradigmatic potential, we introduce the essential fundamentals of MLIPs, including data construction, model training, and hyperparameter optimization. With the developed ACE potentials, we further showcase their applications in the LD modeling of thermal transport for crystalline silicon and amorphous carbon. The corresponding code implementations for MLIP applications in calculating thermal conductivity are also provided for beginners to follow. Cluster expansion ; Statistical physics ; Thermal expansion ; Lattice (music) ; Cluster (spacecraft) ; Thermal ; Materials science ; Chemical physics ; Physics ; Computer science ; Thermodynamics Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Timo N. Schneider | Marcos Gil-García | Markus J. Buehler | Lucas Fugikawa-Santos | Lenka Faltova | Gonzalo Guillén‐Gosálbez | Paolo Arosio
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-11)
Field: Materials Science — Biomaterials

De novo design of peptides localizing at the interface of biomolecular condensates Abstract The interface of biomolecular condensates has been shown to play an important role in processes such as protein aggregation and biochemical reactions. Targeted modulation of these interfaces could, therefore, serve as an effective strategy for engineering condensates and modifying aberrant behaviors. However, the molecular grammar driving the preferential localization of molecules at condensate interfaces remains largely unknown. In this study, we developed a computational pipeline that combines highthroughput coarse-grained simulations, machine learning, and mixed-integer linear programming to design peptides that selectively partition at the interfaces of specific condensate targets. Using this workflow, we designed and synthesized peptides that localize at the interface of three distinct condensates formed by different intrinsically disordered protein regions (IDRs). These peptides exhibit surfactant-like architectures, with one tail incorporated into the condensate and the other excluded from the dense phase. In all cases, the tail entering the condensates is enriched in aromatic residues, while the sequence of the excluded tail varies among the IDRs. For hnRNPA1- and LAF1-IDRs, the excluded tail is enriched in lysines and matches the net charge of the condensate-forming protein, promoting electrostatic repulsion. In the case of DDX4-IDR, which exhibits the lowest charge density, the excluded tail mainly consists of uncharged valine residues, which exhibit negligible interactions with the scaffold protein. These results highlight the importance of the net charge of the scaffold as a key physicochemical parameter for designing peptides with preferential interfacial localization. Overall, our pipeline represents a promising strategy for the rational design of interface-localizing peptides and the identification of the corresponding molecular grammar. Interface (matter) ; Nanotechnology ; Chemistry ; Computational biology ; Computer science ; Biology ; Materials science ; Biochemistry Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Monoclonal and Polyclonal Antibodies Research
Authors: Ruihao Yuan | Bo Wang | Jinshan Li | Peng Sun | Zhen Liu | Xiangdong Ding | Dezhen Xue | Turab Lookman
Source: Acta Materialia (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

Machine learning-enabled design of ferroelectrics with multiple properties via a Landau model Materials science ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Engineering Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices ; Machine Learning in Materials Science
Authors: Tu C. Le | Dzung T. Nguyen | Daniel M. Kamiński | Tony Kolver | Priya Subramanian | Stuart Bateman
Source: ACS Applied Materials & Interfaces (review, 2025-06-26)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning for the Design of Surface Coatings: Challenges and Opportunities Machine learning (ML) has emerged as a transformative tool for the design and optimization of functional materials, offering significant potential to accelerate the discovery and improve performance. In the field of surface coatings, although still in its early stages, ML is increasingly being applied to create novel coating materials with enhanced properties such as adhesion, hardness, durability, and corrosion inhibition. By using data-driven approaches, researchers can optimize formulations and processing conditions more efficiently than traditional trial-and-error methods, paving the way for innovation in advanced coatings that meet specific requirements. This review paper explores the current applications of ML in surface coating research, emphasizing successful case studies that demonstrate its effectiveness in tailoring and enhancing coating properties. The paper also identifies key opportunities and challenges for further integrating ML into coating design workflows. Coating ; Materials science ; Transformative learning ; Workflow ; Nanotechnology ; Durability ; Systems engineering ; Computer science ; Mechanical engineering ; Process engineering ; Engineering ; Composite material Machine Learning in Materials Science ; Surface Modification and Superhydrophobicity ; Thermal properties of materials
Authors: Zihao Yan | Jiankang Li | Sen Zhou | Xiaoming Yang
Source: Carbon (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Machine learning-assisted controllable preparation of multicolor phosphorescent carbon dots Phosphorescence ; Materials science ; Carbon fibers ; Nanotechnology ; Computer science ; Composite material ; Optics ; Physics ; Fluorescence ; Composite number Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications
Authors: Gaoyuan Lu | Shuling Xu | Penghsuan Huang | Lingjun Li
Source: PROTEOMICS (review, 2025-08-13)
Field: Chemistry — Spectroscopy

Enhancing Lipidomics With High‐Resolution Ion Mobility‐Mass Spectrometry Lipids, indispensable yet structurally intricate biomolecules, serve as critical regulators of cellular function and disease progression. Conventional lipidomics, constrained by limited resolution for isomeric and low-abundance species, has been transformed by ion mobility-mass spectrometry (IM-MS). This technology augments analytical power through enhanced orthogonal separation, collision cross-section (CCS)-based identification, and improved sensitivity. This review examines the transformative advances in IM-MS-driven lipidomics, focusing on three major pillars: (1) a critical evaluation of leading ion mobility spectrometry (IMS) platforms, emphasizing innovative instrument geometries and breakthroughs in resolving lipid isomers; (2) an exploration of lipid CCS databases and predictive frameworks, spotlighting computational modeling and machine learning strategies that synergize experimental data with molecular representations for high-confidence lipid annotation; (3) emerging multi-dimensional lipidomics workflows integrating CCS with liquid chromatography-MS/MS to boost identification and depth, alongside mass spectrometry imaging for spatially resolved lipidomics. By unifying cutting-edge instrumentation, computational advances, and biological insights, this review outlines a roadmap for leveraging IM-MS to unravel lipidome complexity, catalyzing biomarker discovery and precision medicine innovation. Lipidomics ; Ion-mobility spectrometry ; Mass spectrometry ; Chromatography ; Resolution (logic) ; Chemistry ; Ion ; Analytical Chemistry (journal) ; Computer science ; Biochemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Chushu Shen | Karandeep Cheema | Yibin Xie | Dan Ruan | Debiao Li
Source: NMR in Biomedicine (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Accelerating CEST MRI With Deep Learning‐Based Frequency Selection and Parameter Estimation Chemical exchange saturation transfer (CEST) MRI is a powerful molecular imaging technique for detecting metabolites through proton exchange. While CEST MRI provides high sensitivity, its clinical application is hindered by prolonged scan time due to the need for imaging across numerous frequency offsets for parameter estimation. Since scan time is directly proportional to the number of frequency offsets, identifying and selecting the most informative frequency can significantly reduce acquisition time. We propose a novel deep learning-based framework that integrates frequency selection and parameter estimation to accelerate CEST MRI. Our method leverages channel pruning via batch normalization to identify the most informative frequency offsets while simultaneously training the network for accurate parametric map prediction. Using data from six healthy volunteers, channel pruning selects 13 informative frequency offsets out of 53 without compromising map quality. Images from selected frequency offsets were reconstructed using the MR Multitasking method, which employs a low-rank tensor model to enable under-sampling of k-space lines for each frequency offset, further reducing scan time. Predicted parametric maps of amide proton transfer (APT), nuclear overhauser effect (NOE), and magnetization transfer (MT) based on these selected frequencies were comparable in quality to maps generated using all frequency offsets, achieving superior performance compared to Fisher information-based selection methods from our previous work. This integrated approach has the potential to reduce the whole-brain CEST MRI scan time from the original 5:30 min to under 1:30 min without compromising map quality. By leveraging deep learning for frequency selection and parametric map prediction, the proposed framework demonstrates its potential for efficient and practical clinical implementation. Future studies will focus on extending this method to patient populations and addressing challenges such as B<sub>0</sub> inhomogeneity and abnormal signal variation in diseased tissues. Computer science ; Artificial intelligence ; Parametric statistics ; Pattern recognition (psychology) ; Deep learning ; Magnetization transfer ; Magnetic resonance imaging ; Mathematics ; Statistics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Amit Kumar | Ali Akbar | Hannah Lesmes | Seán R. Kavanagh | David O. Scanlon | Jakoah Brgoch
Source: Chemistry of Materials (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Discovery of Cr<sup>3+</sup>-Based Near-Infrared Phosphors Cr<sup>3+</sup>-substituted inorganic phosphors exhibit three distinct near-infrared (NIR) photoluminescence emission peak shapes that typically fall between 650 and 950 nm. The exact position and shape are governed by the (weak, intermediate, or strong) crystal field splitting environment of the octahedrally coordinated Cr<sup>3+</sup> ions. These emission characteristics are commonly quantified by the Dq/<i>B</i> ratio, where Dq represents the crystal field splitting parameter and <i>B</i> is the Racah parameter. Precise knowledge of this ratio is therefore critical for designing Cr<sup>3+</sup>-based NIR phosphors for applications like biomedical imaging, night vision, food quality analysis, and luminescence thermometry. Unfortunately, targeting specific Dq/<i>B</i> values in the solid state remains nontrivial due to the complex interplay between the composition, structure, and local coordination environment. To address this challenge, we developed a machine-learned regression model capable of predicting Dq/<i>B</i> trained on 193 experimentally determined Dq/<i>B</i> values and their associated compositional and structural features. We then applied it to estimate the Dq/<i>B</i> values of over 6060 known inorganic structures with potential octahedral Cr<sup>3+</sup> substitution sites. Eight phosphor hosts, Y<sub>2</sub>Mg<sub>3</sub>Ge<sub>3</sub>O<sub>12</sub>, YInGe<sub>2</sub>O<sub>7</sub>, LiInW<sub>2</sub>O<sub>6</sub>, Gd<sub>3</sub>SbO<sub>7</sub>, Ba<sub>2</sub>ScTaO<sub>6</sub>, Ba<sub>2</sub>MgWO<sub>6</sub>, LiLaMgWO<sub>6</sub>, and Ca<sub>3</sub>MgSi<sub>2</sub>O<sub>8</sub>, representing a range of crystal field environments were selected from this list for synthesis and characterization. Their measured Dq/<i>B</i> values closely match model predictions, demonstrating the utility of this machine-learning framework for accelerating the discovery of application-specific Cr<sup>3+</sup>-substituted NIR phosphors. Phosphor ; Photoluminescence ; Luminescence ; Octahedron ; Crystal (programming language) ; Crystal structure ; Solid-state lighting ; Field (mathematics) Luminescence Properties of Advanced Materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Yichen Zhu | Xiaojian Ma | Ruiquan Zhou | Yuwei Sun | Mindi Zhang
Source: Ultrasonics Sonochemistry (review, 2025-03-10)
Field: Materials Science — Materials Chemistry

Bubble collapse dynamics near the composite walls: Progress and challenges Bubble dynamics near the composite walls has become one of the major issues in the fields of aerospace, underwater weapons, and mechanical engineering. The present work reviews recent progress made towards developing experimental and numerical investigation for interaction of bubble dynamics and composite response. The goal of our overall efforts is to (1) summarize the progress made in the experimental and numerical modeling and approaches for bubble dynamics near various composite walls, (2) discuss the effect of designability of the composite materials on the bubble dynamics, with special emphasis on the variations of fiber orientation and ply number of composite walls, as well as correspondingly accompanied by tilted jets and opposite migration of bubbles, with experimental and numerical modeling and approaches, (3) improve the understanding of relationship between bubble dynamic behaviors and material's specific stiffness via experimental data and modified deep neural network method, with particular emphasis on the critical condition of bubble migration under the actions of various material properties. Issues including the mechanism of bubble-wall interaction are discussed. Bubble ; Composite number ; Mechanics ; Materials science ; Composite material ; Physics Ultrasound and Cavitation Phenomena ; Fluid Dynamics and Heat Transfer ; Particle Dynamics in Fluid Flows
Authors: Kalingarayanpalayam Matheswaran Arun Kumar | Tzyy‐Jiann Wang | Steve Selvin | Yu‐Hsu Chang
Source: Journal of environmental chemical engineering (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Machine learning assisted ultra-sensitive SERS identification of multiple nitrofuran antibiotics in complex matrices using urchin-like Ag@MnCo2O4 composites Nitrofuran ; Identification (biology) ; Materials science ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Composite material ; Biology Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: A. A. Gomes | Paulo Henrique Gonçalves Dias Diniz | David Fernandes | Rocío Ríos‐Reina | Silvana M. Azcarate | Ivan Špánik
Source: Food Chemistry (review, 2025-04-27)
Field: Chemistry — Analytical Chemistry

Digital image-based chemometrics for food analysis: a practical tutorial and roadmap Chemometrics ; Computer science ; Digital image analysis ; Hyperspectral imaging ; Chemistry ; Artificial intelligence ; Machine learning ; Computer vision Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Isotope Analysis in Ecology