jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiwei Chen | Changjian Zhou | Yingjie Luo | Wenbo Li | Xiankai Lin | Chunlei Zhang | Siyu Liao | Ruolan Wen | Guitian Qiu | Qian Zhang | Jianxian Yi | Wei‐Hua Lei | Lin Wang | Syed Rizwan | Pei Lin | Qijie Liang
Source: Nano Letters (article, 2025-05-16)
Field: Materials Science — Biomaterials

Artificial Axon with Dendritic-like Plasticity by Biomimetic Interface Engineering of Anisotropic Two-Dimensional Tellurium Spiking neural network (SNN) hardware relies on implicit assumptions that prioritize dendritic/synaptic learning above axon/synaptic concerns, compromising performances in signal capacity, accuracy, and compactness of SNN systems. Herein, we develop an artificial axon by utilizing the heterogeneity and interface state tunability in anisotropic two-dimensional (2D) tellurium (Te). By operating a multiterminal axon under the bioelectricity level, the device achieved neuron-like heterogeneous axon dynamics expansion (∼258%). An excellent dendritic-like tunability (∼197%) exhibits gain on the axons. The synergistic axon-dendrite optimization device exhibits 5-bit programmable conductance, signal filtering, and input enhancing. The accuracy of recognizing data sets based on the SNN algorithm demonstrates efficient optimization (5.2% higher accuracy) of networks by the device features, especially in the case of performing image preprocessing. This artificial neuron solution with anisotropic 2D materials utilizing biomimetic interface engineering provides a universal strategy for compact, high-precision parallel architecture of SNN hardware. Tellurium ; Anisotropy ; Plasticity ; Materials science ; Nanotechnology ; Axon ; Biomimetics ; Interface (matter) ; Composite material ; Biology ; Physics ; Neuroscience ; Optics Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics
Authors: Yuzhou Wang | D.K.L. Tsang | Yibo Zhang | Qiang Zhang | F.R Zhu | Ligang Song | Xianfeng Ma
Source: Nuclear Engineering and Technology (article, 2025-02-23)
Field: Materials Science — Materials Chemistry

Insights into irradiation creep coefficient in nuclear graphite from machine learning Understanding irradiation induced creep in nuclear graphite is critical for the service life extension of current reactor fleet and the technological advancement of next generation nuclear reactors. Nevertheless, qualifying a new graphite grade with respect to irradiation creep requires years of testing and expensive facilities for experiments. Here for the first time, we applied machine learning (ML) algorithms to investigate the irradiation creep coefficient in the secondary stage of graphite creep in hope of gaining new insights and expediting the qualification process. Four ML models were trained on a small dataset with temperature and materials properties as input. The gradient boosting regression model exhibits the best predicting performance. The ML models indicate that temperature and Young's modulus are the most important parameters in the determination of creep coefficients while the rest properties have much weaker impact. These findings align with previous theories and corroborate a creep mechanism governed by dislocation climb, demonstrating the potential of ML in improving the workflow of graphite qualification for advanced reactors. Nuclear graphite ; Creep ; Graphite ; Irradiation ; Materials science ; Nuclear engineering ; Nuclear physics ; Engineering ; Composite material ; Physics Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Radiation Shielding Materials Analysis
Authors: Mingyu Jiang | Zhiling Zhu
Source: Accounts of Materials Research (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Rational Design of Nanozymes for Catalytic Medicine ConspectusRecent advances in nanozymes have opened up new paradigms for catalytic medicine, offering promising avenues for disease intervention. However, the clinical translation of nanozyme-based therapies remains hindered by several key limitations, including insufficient catalytic efficiency, limited substrate selectivity, and instable functional performance in vivo. This Account proposes a disease-oriented, data-driven strategy for the rational design of nanozymes. By constructing a set of physicochemical descriptors closely linked to therapeutic demands and integrating experimental data, materials databases, theoretical simulations, and machine learning algorithms, it aims to establish robust structure–function–efficacy relationships for nanozyme systems. This approach enables precise performance prediction and targeted optimization of nanozymes. Furthermore, it discusses the emerging trends in rational nanozyme design for catalytic medicine and highlights the key scientific challenges. Ultimately, this Account advocates a paradigm shift from “function-driven” to “disease-oriented” design principles and outlines a theoretical and technological framework for the development of high-efficiency and precision nanozymes in biomedical applications. Rational design ; Key (lock) ; Design elements and principles ; Set (abstract data type) ; Clinical Practice Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Yue Ren | Yilin Wang | Yuxin Zhang | Delang Xie | Jiajun Guo | Mei Guo | Yang Xu | Xingchen Kong | Yuqin Feng | Jianguo He | Guishan Liu
Source: Trends in Food Science & Technology (review, 2025-07-01)
Field: Materials Science — Materials Chemistry

Plant-based source, preparation, application, and prospect of carbon dots in multifunctional food packaging: A comprehensive review of recent advances Food packaging ; Nanotechnology ; Carbon fibers ; Materials science ; Environmental science ; Engineering ; Mechanical engineering ; Composite material Carbon and Quantum Dots Applications ; Graph Labeling and Dimension Problems
Authors: Tuo Leng | Yuting Wang | Lizhi Huang | Qiang Yu | Jianhua Xie | Yi Chen
Source: Food Research International (article, 2025-06-09)
Field: Chemistry — Analytical Chemistry

Predicting oxidative stability of Camellia oil based on multimodal data fusion strategy integrating NIR and Raman spectroscopies with chemometrics Chemometrics ; Raman spectroscopy ; Chemistry ; Fusion ; Hyperspectral imaging ; Chromatography ; Computer science ; Artificial intelligence ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Nazar Riswana Barveen | Che-Wei Zeng | Yu‐Wei Cheng
Source: Food Chemistry (article, 2025-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-integrated SERS platform based on Au Se nanowires for indigo carmine detection in real samples Indigo carmine ; Nanotechnology ; Chemistry ; Materials science ; Nuclear chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Identification and Quantification in Food ; Advanced Nanomaterials in Catalysis
Authors: Ning Feng | Y. Song | Yuanyuan Li | Xinyi Cheng | Qing Yu | Jianbo Shi | Huajian Gao | Jin Xie | Qian Zhang | Yan-Biao Chen | Ce Tang | Yanmin Zhang
Source: Trends in Food Science & Technology (article, 2025-09-12)
Field: Chemistry — Spectroscopy

Versatile platforms based on HPTLC: Multimodal and green solutions for food and herbal quality assurance Quality assurance ; Automation ; Quality (philosophy) ; Emerging technologies ; Reuse ; Sustainability ; Food safety Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Xiang‐Yang Liu | Xiang Qian | Sheng-Rui Wang | Dongsheng Xiao | Wei‐Hai Fang | Ganglong Cui
Source: The Journal of Physical Chemistry Letters (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes In this work, we present a machine learning (ML) approach for predicting the optimal range separation parameters in transition metal complexes (TMCs), aiming to reduce the computational cost associated with optimally tuned range-separated hybrid (OT-RSH) functionals while preserving their accuracy. A data set containing 4380 TMCs was constructed by screening the tmQM database, with each TMC represented by a 62 087-dimensional multiple-fingerprint feature (MFF) vector and labeled with its optimally tuned range separation parameter. Multiple regression models were applied to train the prediction model, and the support vector machine (SVM) model yielded the best performance. The resulting machine-learned functional, ML-LC-PBE0*, achieves ionization potentials comparable to those obtained from fully tuned LC-PBE0* calculations but at a reduced computational cost. Furthermore, comparisons with DLPNO-CCSD(T) reference values confirm that ML-LC-PBE0* consistently outperforms conventional global and fixed parameter RSH functionals. This work provides an efficient and accurate framework for high-throughput screening and electronic structure prediction of TMCs, paving the way for the discovery of advanced TMC-based optoelectronic materials. Support vector machine ; Range (aeronautics) ; Set (abstract data type) ; Feature (linguistics) ; Work (physics) ; Data set ; Regression ; Transition metal Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Organic Light-Emitting Diodes Research
Authors: Zezhu Zeng | Xia Liang | Zheyong Fan | Yue Chen | Michele Simoncelli | Bingqing Cheng
Source: ACS Materials Letters (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Thermal Transport of Amorphous Hafnia Across the Glass Transition Heat transport in glasses over a wide temperature range is critical for applications in gate dielectrics and thermal insulators but remains poorly understood due to the challenges in modeling vibrational anharmonicity and configurational dynamics across the glass transition. Recent predictions show an unusual decrease in thermal conductivity (κ) with temperature in amorphous hafnia (a-HfO<sub>2</sub>), contrasting with the typical trend in glasses. Using molecular dynamics with a machine-learning-based neuroevolution potential, we compute κ of a-HfO<sub>2</sub> from 50 K to 2000 K. At low temperatures, the Wigner transport equation captures both anharmonicity and quantum statistics. Above 1200 K, atomic diffusion invalidates the quasiparticle picture, and we resort to the Green-Kubo method to capture convective transport. We further extend the Wigner transport equation to supercooled a-HfO<sub>2</sub>, revealing the crucial role of low-frequency modes in facilitating heat transport. The computed κ, based on both Green-Kubo and Wigner transport theories, increases continuously with temperature up to 2000 K. Hafnia ; Amorphous solid ; Glass transition ; Materials science ; Thermal ; Chemical physics ; Mineralogy ; Chemistry ; Thermodynamics ; Crystallography ; Composite material ; Physics ; Polymer Phase-change materials and chalcogenides ; Thermal properties of materials ; Glass properties and applications
Authors: Bo Zhang | Duoqi Shi | Changqi Liu | Qianni Wang | Yangyi Yu | Zhenyu Wang | Xiaoguang Yang
Source: Composite Structures (article, 2025-06-21)
Field: Materials Science — Ceramics and Composites

Quantification evaluation of tensile damage in SiO2f/SiO2 ceramic matrix composites based on X-ray computed tomography and acoustic emission techniques Acoustic emission ; Materials science ; Ultimate tensile strength ; Composite material ; Ceramic matrix composite ; X-ray ; Computed tomography ; Tomography ; Matrix (chemical analysis) ; Ceramic ; Radiology ; Optics ; Physics ; Medicine Advanced ceramic materials synthesis ; Advanced materials and composites ; Advanced Surface Polishing Techniques
Authors: Weiyu Guo | Long Wang | Chuantao Hou | Yonglong Du | Chao Chen | Daxu Zhang
Source: Composites Part B Engineering (article, 2025-09-01)
Field: Materials Science — Ceramics and Composites

Deep-learning-based image-driven modelling of woven ceramic matrix composites: incorporating real void morphology and damage-induced behaviour Materials science ; Composite material ; Void (composites) ; Ceramic matrix composite ; Ceramic ; Morphology (biology) ; Porosity ; Matrix (chemical analysis) Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Yongxin Cui | Hong-an Lu | Jieru Sun | Qian Zou | Junhao Ma | Chonggui Zhong | Huailiang Fu | Lei Zhang | Pengxia Zhou
Source: Physica Scripta (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

SHAP-based interpretable machine learning for predicting the oxide double perovskite band gaps Abstract Oxide double perovskite has excellent optical properties. In recent years, using machine learning (ML) to study the provskite properties has become a hot topic, however, the corresponding model and characteristic explanation have been missed. We propose a ML-based method to interprete the feature importance of band gap in the oxide double perovskite from the global level by using multiple models and to validate the result from the local level. Four different ML algorithms Random Forest (R 2 :0.883, RMSE:0.535), Gradient Boosting Regression (R 2 :0.835, RMSE:0.639), Decission Tree Regression (R 2 :0.801, RMSE:0.712), and eXtreme Gradient Boosting (R 2 :0.888, RMSE: 0.528) are used, which have good predictive performance. The results suggest that the average number of valence electrons in the d- orbitals (mean NdValence), the maximum melting point (maximum MeltingT), and the range of unfilled valence electrons(range NUnfilled) of the constituent elements in the material have the greatest influence on the band gap in the oxide double perovskite. It has a positive effect on the band gap when the mean NdValence is less than 1.5, and a negative effect when the mean NdValence is greater than 1.5. When the range of NUnfilled is less than or equal to 6, it negatively affects the band gap. In addition, by using models to explain specific materials, we found that for small band gap and large band gap materials, the features have negative and positive effects, respectively, the result is further supported by the calculated SHAP value of different range of band gaps. Our method provides valuable insights for the application of oxide double perovskites in the field of photovoltaics. Perovskite (structure) ; Materials science ; Oxide ; Artificial intelligence ; Computer science ; Machine learning ; Chemical engineering ; Metallurgy ; Engineering Machine Learning in Materials Science
Authors: Behzad Hashemi Soudmand | Amirhossein Najafi | Rasool Mohsenzadeh | Karim Shelesh‐Nezhad
Source: Composites Science and Technology (article, 2025-08-25)
Field: Materials Science — Polymers and Plastics

A scalable, dispersion-aware framework for quantifying clustering severity in polymer nanocomposites and assessing mechanical impact Materials science ; Composite material ; Dispersion (optics) ; Nanocomposite ; Polymer nanocomposite ; Cluster analysis ; Polymer ; Computer science ; Artificial intelligence Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Asphalt Pavement Performance Evaluation
Authors: Siyu Li | Chang Li | Xuan Wang | Pengfeï Liu | Xing Han
Source: Journal of Thermal Spray Technology (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Evaluation and Analysis of Particle Oxidation of HVOF Thermal Spraying Based on GA-BP Neural Network Algorithm Thermal spraying ; Materials science ; Artificial neural network ; Thermal ; Particle (ecology) ; Metallurgy ; Composite material ; Computer science ; Artificial intelligence ; Coating Catalytic Processes in Materials Science ; Surface Roughness and Optical Measurements ; Industrial Gas Emission Control
Authors: Anna Lueber | Konstantin Karchev | Chloe Fisher | Matthew David Heim | Roberto Trotta | Kevin Heng
Source: The Astrophysical Journal Letters (article, 2025-04-28)
Field: Chemistry — Spectroscopy

Near-instantaneous Atmospheric Retrievals and Model Comparison with <tt>FASTER</tt> Abstract In the era of the James Webb Space Telescope (JWST), the dramatic improvement in the spectra of exoplanetary atmospheres demands a corresponding leap forward in our ability to analyze them: atmospheric retrievals need to be performed on thousands of spectra, applying to each large ensembles of models (that explore atmospheric chemistry, thermal profiles, and cloud models) to identify the best one(s). In this limit, traditional Bayesian inference methods such as nested sampling become prohibitively expensive. We introduce Fast Amortized Simulation-based Transiting Exoplanet Retrieval ( FASTER ), a neural-network-based method for performing atmospheric retrieval and Bayesian model comparison at a fraction of the computational cost of classical techniques. We demonstrate that the marginal posterior distributions of all parameters within a model and the posterior probabilities of the models we consider match those computed using nested sampling both on mock spectra and for the real NIRSpec PRISM spectrum of WASP-39b. The true power of the FASTER framework comes from its amortized nature, which allows the trained networks to perform practically instantaneous Bayesian inference and model comparison over ensembles of spectra—real or simulated—at minimal additional computational cost. This offers valuable insight into the expected results of model comparison (e.g., distinguishing cloudy from cloud-free and isothermal from nonisothermal models), as well as their dependence on the underlying parameters, which is computationally unfeasible with nested sampling. This approach will constitute as large a leap in spectral analysis as the original retrieval methods based on Markov Chain Monte Carlo have proven to be. Environmental science ; Atmospheric sciences ; Remote sensing ; Meteorology ; Geography ; Physics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Stellar, planetary, and galactic studies
Authors: Jutta Geier | Chiara Barretta | Mario Messiha | Márton Bredács | Florian Arbeiter | Gerald Koinig | Eric Helfer | L. Meinhart | Gernot Oreški
Source: Waste Management (article, 2025-06-18)
Field: Chemistry — Analytical Chemistry

Detection of degraded high-density polyethylene via near-infrared hyperspectral imaging The quality of recycled plastics is crucial to make them competitive in more demanding applications and to extend their range of applications. However, there are many influencing factors that can reduce the quality and limit the use of recyclates. One of these factors is degradation, which can occur at different stages of a plastic's life cycle. Degraded material can affect the quality of recyclates. Therefore, it would be beneficial to sort out heavily aged plastics from the recycling stream before further processing. This work investigates the possibility of separating severely degraded polyethylene (PE) from unaged or less degraded PE using near-infrared (NIR) hyperspectral imaging. For this purpose, PE samples were artificially aged using two methods: (i) exposure to UV light, (ii) exposure to aqueous chlorine dioxide solution. The ageing state of the samples was assessed by means of Fourier Transform Infrared (FTIR) spectroscopy and tensile tests and their NIR spectra were recorded on a laboratory NIR sorter. The ability to separate highly degraded from non-/less-degraded samples, which was defined by their mechanical performance, was then analysed using multivariate data analysis and machine learning algorithms applied to the NIR data. These analyses showed promising results for separating highly degraded PE samples, with the classification between degraded and less degraded PE achieving accuracy and F1 scores exceeding 90%. Hyperspectral imaging ; Polyethylene ; Fourier transform infrared spectroscopy ; Near-infrared spectroscopy ; Materials science ; Chemical imaging ; Degradation (telecommunications) ; Environmental science ; Computer science ; Artificial intelligence ; Chemical engineering ; Composite material ; Optics ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Microplastics and Plastic Pollution
Authors: Ruifen Zhang | Qiyang Zhang | Yin Wu
Source: NeuroImage (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

A CVAE-based generative model for generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si41.svg" display="inline" id="d1e678"> <mml:msub> <mml:mrow> <mml:mi>B</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> </mml:math> inhomogeneity corrected chemical exchange saturation transfer MRI at 5 T Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful tool to image endogenous or exogenous macromolecules. CEST contrast highly depends on radiofrequency irradiation B<sub>1</sub> level. Spatial inhomogeneity of B<sub>1</sub> field would bias CEST measurement. Conventional interpolation-based B<sub>1</sub> correction method required CEST dataset acquisition under multiple B<sub>1</sub> levels, substantially prolonging scan time. The recently proposed supervised deep learning approach reconstructed B<sub>1</sub> inhomogeneity corrected CEST effect at the identical B<sub>1</sub> as of the training data, hindering its generalization to other B<sub>1</sub> levels. In this study, we proposed a Conditional Variational Autoencoder (CVAE)-based generative model to generate B<sub>1</sub> inhomogeneity corrected Z spectra from single CEST acquisition. The model was trained from pixel-wise source-target paired Z spectra under multiple B<sub>1</sub> with target B<sub>1</sub> as a conditional variable. Numerical simulation and healthy human brain imaging at 5 T were respectively performed to evaluate the performance of proposed model in B<sub>1</sub> inhomogeneity corrected CEST MRI. Results showed that the generated B<sub>1</sub>-corrected Z spectra agreed well with the reference averaged from regions with subtle B<sub>1</sub> inhomogeneity. Moreover, the performance of the proposed model in correcting B<sub>1</sub> inhomogeneity in APT CEST effect, as measured by both MTR<sub>asym</sub> and [Formula: see text] at 3.5 ppm, were superior over conventional Z/contrast-B<sub>1</sub>-interpolation and other deep learning methods, especially when target B<sub>1</sub> were not included in sampling or training dataset. In summary, the proposed model allows generalized B<sub>1</sub> inhomogeneity correction, benefiting quantitative CEST MRI in clinical routines. Mathematics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis