jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chen Yijia | MA Zhiyuan | Zhang Tianxu | Lin Li
Source: SHILAP Revista de lepidopterología (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Research progress in microstructure characterization and reconstruction of heterogeneous materials Heterogeneous materials are widely used in high-end equipment components of fields such as aerospace，electronic information and national defense，with the microstructure features of multi-phase，multi-scale and complex morphology，which determines the superior macroscopic properties of materials. Establishing an accurate microstructure model is crucial for deeply understanding the structure-property relationship. However，when the material exhibits strong heterogeneous characteristics，the complexity and difficulty of this process increase significantly. In recent years，advances in computational materials science promote the development of computational simulation methods. Microstructure characterization and reconstruction（MCR），as a key component of computational simulation process，provides a powerful approach for microstructure modeling of heterogeneous materials. Currently，MCR for heterogeneous materials mainly includes the following two categories：（1）modeling techniques based on statistical methods；（2）modeling techniques based on machine learning and computer vision. This work summarizes and organizes two categories of MCR techniques，explains the characteristics and applicability of the relevant methods，and analyzes the research progress of different methods in MCR of heterogeneous material. It aims to provide references and guidance for selecting MCR methods and applying them to material design. Characterization (materials science) ; Microstructure ; Component (thermodynamics) ; Process (computing) ; Key (lock) ; Computational model Machine Learning in Materials Science ; High-Velocity Impact and Material Behavior ; Composite Material Mechanics
Authors: Matthew Niichel | R. P. Madden | Hannah Pike | Nafees Bin Kabir | True Miller | Brian Jowers | Stylianos Chatzidakis
Source: Preprints.org (preprint, 2025-02-13)
Field: Materials Science — Materials Chemistry

&lt;span style="mso-bidi-font-size: 18.0pt; border: none windowtext 1.0pt; mso-border-alt: none windowtext 0cm; padding: 0cm;"&gt;Design and Characterization of the Modified Purdue Subcritical Pile for Nuclear Research Applications&lt;span style="mso-fareast-language: EN-US;"&gt; First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize its neutron and gamma flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5*10^5 cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive dataset with the potential for machine learning applications. The neutron flux throughout the pile is calculated using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistic accurate Monte-Carlo model using OpenMC. The experimental measurements reveal the testbed has a neutron environment with a total neutron flux approaching 8.5*10^3 n/cm^2*s and a thermal flux of 5.8*10^3 n/cm^2*s, following the expected diffusion theory behavior. This work establishes the modified Purdue subcritical pile can provide significant neutron and gamma fluxes and a uniquely large volume to enable radiation testing of integral electronic components and as a versatile research instrument with the potential to support microelectronics testing, limited isotope production, and non-destructive imaging while generating valuable training datasets for machine learning algorithms in nuclear applications. [M1]Reference citation is not allowed. Please revise. Padding ; Characterization (materials science) ; Pile ; Span (engineering) ; Structural engineering ; Materials science ; Engineering ; Composite material ; Nanotechnology Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Jaebum Noh (11038176) | Jaekyung Kim (5945468) | Junsuk Rho (4436116)
Source: Figshare (article, 2025-07-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

OvercomingInformation Sparsity in Metasurfaces forFull-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. Holography ; RGB color model ; Multiplexing ; Transmission (telecommunications) ; Artificial neural network ; Phase (matter) ; Computer-generated holography Metamaterials and Metasurfaces Applications ; Photorefractive and Nonlinear Optics ; Advanced Optical Imaging Technologies
Authors: Adedotun Adetunla | Tien-Chien Jen
Source: Advances in Materials Science and Engineering (article, 2025-01-01)
Field: Materials Science — Biomaterials

Analyzing the Mechanical Behavior of Magnesium Metal Matrix Composites Fabricated Through Friction Stir Processing Magnesium alloys show promise for orthopedic implants due to biodegradability and biocompatibility, but rapid degradation limits their use. This study fabricates AZ31 magnesium composites reinforced with CaCO 3 powder via friction stir processing (FSP). Three conditions were tested: one‐pass reinforced (Sample A), three‐pass reinforced (Sample B), and three‐pass unreinforced (Sample C). Mechanical characterization included tensile, hardness, impact, and wear tests, while degradation was assessed in blood and plasma media. Sample B exhibited superior tensile strength (306.13 MPa) and the highest impact strength (0.1352 J/mm 2 ), though hardness decreased relative to Sample C. Degradation rates were 0.033 g/day (blood) and 0.031 g/day (plasma), with Sample B showing slower plasma degradation. Machine learning models—Linear Regression and Lasso Regression—were applied to predict long‐term degradability. Linear Regression achieved lower prediction error (7.69 days vs. 13.75 days for Lasso), forecasting full degradation of a 71.5 g implant in ∼2190 days (6 years). The integrated experimental–ML framework supports optimized design of biodegradable implants with predictable lifespans. This predictive model could help plan maintenance and ensure implant safety, potentially reducing the need for second surgeries. Ultimate tensile strength ; Magnesium ; Degradation (telecommunications) ; Matrix (chemical analysis) ; Friction stir processing ; Cubic zirconia ; Biodegradation Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Welding Techniques Analysis
Authors: Mandi Rong | Tong Jiang | Hao Ding | Zhixiang Huang | Li Ding | Ming Fang
Source: Laser & Photonics Review (article, 2025-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Long Wave Infrared Light Modulation Based on Metalenses: Principles, Progress, and Challenges ABSTRACT Long‐wave infrared (LWIR) metalenses, capable of precise wavefront modulation through subwavelength meta‐atoms, represent a transformative advancement in infrared optics, offering a compact alternative to conventional refractive elements. This work systematically examines the fundamental principles, typical designs, and fabrication challenges of LWIR metalenses, with an emphasis on different phase modulation strategies, presenting their distinct advantages and limitations. Recent breakthroughs in aberration correction, wide‐field imaging, and polarization‐sensitive functionalities are critically reviewed, alongside computational optimization approaches such as deep learning and topology algorithms. Fabrication techniques, including advanced lithography and etching, are evaluated for their scalability and precision in realizing high‐performance metasurfaces. Persistent challenges in efficiency, thermal stability, and large‐scale manufacturing underscore the need for novel materials, refined nanofabrication methods, and integrated design frameworks. By addressing these barriers, LWIR metalenses hold potential to revolutionize applications in thermal imaging, defense systems, and biomedical sensing, paving the way for next‐generation infrared technologies. Wavefront ; Fabrication ; Scalability ; Modulation (music) ; Lithography ; Nanolithography ; Infrared ; Nanoimprint lithography Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Optical Coatings and Gratings
Authors: Jing Zhang | Mengting Ning | Haolin Chen | Jiaqi Liu | Jieyi Chen | Dantong Shen | Zhouyi Guo | Bo Xu | Yan‐Jiao Zhang | Zhiming Liu
Source: Laser & Photonics Review (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

BandGap‐Tunable Carbon Quantum Dots for Surface‐Enhanced Raman Scattering ABSTRACT Carbon dots (CDs) with photoluminescence properties have been widely recognized; however, their inherent surface‐enhanced Raman scattering (SERS) performance is rarely reported. Herein, we propose a type of bandgap‐tunable carbon quantum dots (CQDs) derived from metal‐phenolic network (MPN) that exhibits excellent and controllable SERS performance. MPN‐CQDs may be the ideal non‐metallic SERS substrates to accurately elucidate the chemical mechanism (CM) due to their simply controllable bandgap structure. By screening the coordinated metal ions of MPN, MPN‐CQDs can realize the optimal SERS effect with the maximum Raman enhancement factor of 5.5 × 10 4 , and also exhibit outstanding SERS reproducibility and stability. We then systematically disclose the interfacial photo‐induced charge transfer process and corresponding migration pathways between bandgap‐tunable MPN‐CQDs and the analyte. This class of nonmetallic SERS substrates is finally applied for the detection of hemoglobin with high sensitivity; further combined with a machine learning algorithm, we have successfully achieved precise identification of the heterogeneity of hemoglobin. This is the first evidence for the tunable SERS performance in CQDs, which also offers a facile avenue for in‐depth understanding of CM in nonmetallic materials. Raman scattering ; Carbon quantum dots ; Raman spectroscopy ; Quantum dot ; Photoluminescence ; Carbon fibers ; Scattering ; Band gap Carbon and Quantum Dots Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Ruotong Chen | Fang Song | Yang Liu | Chuantao Zheng | Hengliang Zhu | Mingquan Pi | Yue Yang | Yiding Wang | Xiue Jiang
Source: Infrared Physics & Technology (article, 2025-07-08)
Field: Chemistry — Analytical Chemistry

An enhanced glucose analysis method based on comprehensive pre-processing methods and multi-feature values-based machine learning Computer science ; Feature (linguistics) ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Min Guan | Guilong Wang | Kuan Li | Xinyi Liu | Zhongying Guo | Mingtian Li
Source: Dyes and Pigments (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Controlled synthesis of multicolor fluorescent carbon quantum dots based on phenylenediamine isomers and their application in nitrite smart sensing and fingerprint trace detection systems Quantum dot ; Fluorescence ; Detection limit ; Linear range ; Fingerprint (computing) ; Carbon quantum dots ; Selectivity ; Carbon fibers ; Absorption (acoustics) Carbon and Quantum Dots Applications ; Forensic Fingerprint Detection Methods ; Luminescence and Fluorescent Materials
Authors: Poobodin Mano | Klichchupong Dabsamut | Ching‐Ming Wei | Siriporn Kosawatthanakun | Bunyarat Rungtaweevoranit | Supawadee Namuangruk‬ | Kaito Takahashi
Source: Journal of the American Chemical Society (article, 2025-12-13)
Field: Chemistry — Inorganic Chemistry

Unraveling the Humidity-Induced Phase Transition in CALF-20 via Machine Learning Potentials CALF-20 is a metal-organic framework (MOF) that is known for its exceptional CO<sub>2</sub> selectivity under humid conditions. However, the molecular mechanism underlying its humidity-induced structural phase transitions and how water and CO<sub>2</sub> compete at the molecular level remain unclear. In this work, we developed a machine learning potential (MLP) with first-principles accuracy to investigate how water dynamics drive the transition from the open-pore (OP) to the closed-pore (CP) phase. We identify that water coordination at the Zn node forms hydrogen-bonded water dimers and trimers along the [011] diffusion channel, which are critical for the OP-CP phase transition. In the CP structure, these Zn-bound water networks exhibit residence times exceeding hundreds of picoseconds and occupy approximately half of the Zn nodes, reproducing previous experiments for the first time. Remarkably, preadsorbed CO<sub>2</sub> disrupts this water network. This effect is further supported by diffuse reflectance infrared Fourier transform spectroscopy, which reveals suppressed O-H stretching signals in CO<sub>2</sub> preloaded samples, indicating the inhibition of hydrogen-bonded water cluster formation. These findings provide a mechanistic explanation for the experimentally observed delay in the water uptake under competitive CO<sub>2</sub>/H<sub>2</sub>O adsorptions. Moreover, our MLP simulations accurately reproduce the water adsorption isotherm and experimental X-ray diffraction patterns of the α, β, τ, and γ phases, establishing a direct link between the microscopic structure and the macroscopic phase behavior observed in experiments. This study provides molecular-level insights into humidity-induced transitions in flexible MOFs and demonstrates a simulation framework for modeling guest-responsive behavior in soft porous materials. Phase transition ; Picosecond ; Water model ; Phase (matter) ; Molecular dynamics ; Diffusion ; Diffraction ; Fourier transform Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: P. Kalra | Michael S. Lawless | Rafał A. Bachorz
Source: Toxicology Letters (article, 2025-09-01)
Field: Chemistry — Spectroscopy

P03-23 A framework for computing TK-equivalence using a large number of environmental compounds In silico ; Autoencoder ; Chemical space ; Euclidean distance ; Key (lock) ; Molecular descriptor ; k-nearest neighbors algorithm ; Latent variable Analytical Chemistry and Chromatography
Authors: Yuwei Pan (11606182) | Ling Zhang (18071) | Lei Sun (45752) | Dong Zhai (107411) | Wenliang Li (187808) | Zhen Li (49109) | Tie Yu (1826302) | Weiqiao Deng (1660159) | Li Yang (6520)
Source: Figshare (article, 2025-11-27)
Field: Chemistry — Inorganic Chemistry

Accelerated Screeningof Zeolites for Methanol-to-PropyleneConversion Using Machine Learning with Interpretable Selectivity Function Methanol-to-propylene (MTP) is one of the important propylene production technologies that is commonly achieved by the use of zeolite catalysts. However, effectively regulating the selectivity toward propylene remains challenging due to the insufficient understanding of the principles behind selectivity control. In this study, we proposed an interpretable function to assess the propylene/ethylene (P/E) selectivity based on a thorough investigation of the MTP selectivity control mechanism and implemented a machine learning (ML) method for high-throughput screening of zeolite frameworks for MTP. Density functional theory and grand canonical Monte Carlo simulations have been used to build the ML model. The P/E selectivity predictions are supported by the reported and our own experiments. We chose Mobil Five (MFI) (rank 374) and Deca-dodecasil 3R (DDR) (rank 5869) for experimental validation. Experimental results showed that the Ga-MFI catalyst achieved a P/E ratio of 12.3 and a remarkably longer operational lifespan of 121.3 h in the MTP process, surpassing previously reported values. Additionally, the marked superiority of Ga-MFI over Ga-DDR, especially in terms of P/E ratios and lifespans, further underscores the effectiveness of our zeolite screening strategy. Selectivity ; Function (biology) ; Zeolite ; Monte Carlo method ; Density functional theory ; Probability density function Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Sofiia
A. Dymura (21727216) | Oleksandr O. Viniichuk (21727219) | Kostiantyn P. Melnykov (6631775) | Dmytro S. Radchenko (1617085) | Oleksandr O. Grygorenko (1617082)
Source: Figshare (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

Machine Learning-BasedRetention Time Prediction Toolfor Routine LC-MS Data Analysis Accurate retention time (<i>R</i><sub>T</sub>) prediction models can significantly improve liquid chromatography–mass spectrometry (LC-MS) data analysis widely used in chemical synthesis. As hundreds of thousands of syntheses are performed annually at Enamine, a large amount of experimental data has been generated internally. In this paper, we present the development of an <i>R</i><sub>T</sub> prediction model based on the GATv2Conv + DL graph neural network (NN) architecture, trained on the internal data and further evaluated using the METLIN SMRT data set. The final model achieved a mean absolute error (MAE) of 2.48 s for the 120 s LC-MS method. We also conducted a detailed analysis of <i>R</i><sub>T</sub> prediction errors and determined that the interval between <i>R</i><sub>T</sub> – 7.12 s and <i>R</i><sub>T</sub> + 9.58 s contained over 95% of the data. The developed model has been successfully integrated into the existing in-house LC-MS analysis toolkit, enhancing its predictive and analytical capabilities. Additionally, we have published a curated subset of 20,000 data points from our internal data set to support community benchmarking and further research. Benchmarking ; Artificial neural network ; Data set ; Interval (graph theory) ; Set (abstract data type) ; Predictive modelling ; Graph ; Data modeling ; Mean squared prediction error Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Yuta Sakai | Shota Horikawa | Mamoru Aizawa | Hiromasa Kaneko
Source: Analytical Science Advances (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Prediction of Bone Formation Rate of Artificial Bone With Machine Learning Models Considering the Variation of Experimental Results The proportion of older people in the world's total population is expected to increase. Bone diseases are more prevalent in older people; therefore, the number of patients with such diseases is expected to increase worldwide. Artificial bone is a biomaterial used in the treatment of bone diseases. Artificial bones with high bone formation rates are desired; however, the results of artificial bone implantation vary. There are also ethical issues associated with animal experiments. Our purpose in this study is to predict the variation in bone formation rates. We created multiple sub-datasets and constructed a machine learning model to predict the variation in bone formation rates by considering the results of multiple measurements. We also propose a metric, Jensen-Shannon (JS) divergence, to evaluate the accuracy of the model for predicting variation. We tested the validity of JS divergence by comparing combinations of explanatory variables. Additionally, we found an optimal combination of explanatory variables to construct a model with high predictive accuracy. We expect that the prediction of variation will be useful for improving the practical development of materials and medicines, such as artificial bones, for which stable effects are required, regardless of the individual. Variation (astronomy) ; Artificial bone ; Computer science ; Artificial intelligence ; Machine learning ; Engineering ; Biomedical engineering ; Physics Spectroscopy and Chemometric Analyses
Authors: Raphael M. Tromer (9040812)
Source: Figshare (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Dynamic Collision Fingerprints (DCF): Introducinga New Descriptor Linking Lattice Interactions to 2D Structural DataSignatures We introduce a novel method for structural characterization of two-dimensional (2D) materials based on classical particle trajectories and elastic collisions with atomic lattices. This approach, called the dynamic collision fingerprint (DCF), encodes structural features through statistical patterns of collisions, capturing symmetry, porosity, and disorder using purely geometric information. Applied to systems such as graphene, phagraphene, CEY-graphene, and h-BN, the method extracts descriptors, including mean free path, diffusivity, angular entropy, and Fourier-based symmetry metrics. These form compact interpretable vectors suitable for classification and machine learning. All calculations were performed on a personal computer, underscoring the method’s efficiency and accessibility. DCF provides a robust and general tool for 2D materials analysis, particularly well-suited for carbon-based and atomically thin systems. Collision ; Lattice (music) ; Fingerprint (computing) ; Symmetry (geometry) ; Signature (topology) ; Characterization (materials science) ; Statistical analysis ; Experimental data Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Thermal properties of materials
Authors: Haojun Zeng | Guokun Yang | Jiahui Du | Jun Jiang | Song Wang
Source: Chinese Chemical Letters (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Cross-database property prediction and multifunctional molecule screening via spectral descriptor Property (philosophy) ; Convex hull ; Molecular descriptor ; Chemical space ; Stability (learning theory) ; Molecule ; Space (punctuation) ; Spectral properties Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks