jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Chen | Yujiao Sun | Zi‐Jiang Yang | Sheng Huang | Xiuquan Gu
Source: Advanced Theory and Simulations (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Gas Response in CuO via First‐Principles Calculations and Machine Learning Abstract Recent advancements in gas‐sensitive materials based on metal oxides have mainly relied on experimental trial and error, which is time‐consuming and costly. To address this, a novel approach combining first‐principles calculations and machine learning is proposed to predict the gas response properties of materials. Copper oxide (CuO) is used as a representative material for validation. Six characteristic parameters are selected at the electron and atomic structure level, including adsorption energy (Eads), bandgap (Eg), distortion degree, conduction band minimum (CBM), valence band maximum (VBM), and bond length (d), to build an accelerated gas response discovery model. The results indicate that gas response is determined by changes in these parameters upon gas adsorption, though no direct correlation is found. Machine learning algorithms are applied to establish correlation models, achieving an accuracy of 83.75%. Analysis reveals that the distortion degree has the most significant impact on a gas response (28.57%), while the VBM contributes the least (4.76%). CuO exhibits a strong response to gases like C 3 H 8 O, C 4 H 10 O, CO, H 2 , and NO 2 , but minimal response to C 6 H1 5 N and C 8 H 10 , consistent with literature findings. This work offers new insights for sensor development and could enhance the efficiency of material discovery in gas sensing applications. Computer science ; Materials science ZnO doping and properties ; Electronic and Structural Properties of Oxides ; Copper-based nanomaterials and applications
Authors: Leqi Yin | Malvika Viswanathan | Yashwant Kurmi | Zhongliang Zu
Source: Physics in Medicine and Biology (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Improving quantification accuracy of a nuclear Overhauser enhancement signal at −1.6 ppm at 4.7 T using a machine learning approach <i>Objective.</i>A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at -1.6 ppm, potentially from choline phospholipids and termed NOE(-1.6), has been reported in biological tissues at high magnetic fields. This signal shows promise for detecting brain tumors and strokes. However, its proximity to the water peak and low signal-to-noise ratio makes accurate quantification challenging, especially at low fields, due to the difficulty in separating it from direct water saturation and other confounding signals. This study proposes using a machine learning (ML) method to address this challenge.<i>Approach.</i>The ML model was trained on a partially synthetic chemical exchange saturation transfer dataset with a curriculum learning denoising approach. The accuracy of our method in quantifying NOE(-1.6) was validated using tissue-mimicking data from Bloch simulations providing ground truth, with subsequent application to an animal tumor model at 4.7 T. The predictions from the proposed ML method were compared with outcomes from traditional Lorentzian fit and ML models trained on other data types, including measured and fully simulated data.<i>Main results.</i>Our tissue-mimicking validation suggests that our method offers superior accuracy compared to all other methods. The results from animal experiments show that our method, despite variations in training data size or simulation models, produces predictions within a narrower range than the ML method trained on other data types.<i>Significance.</i>The ML method proposed in this work significantly enhances the accuracy and robustness of quantifying NOE(-1.6), thereby expanding the potential for applications of this novel molecular imaging mechanism in low-field environments. Ground truth ; Computer science ; Robustness (evolution) ; Artificial intelligence ; Transfer of learning ; SIGNAL (programming language) ; Nuclear Overhauser effect ; Biological system ; Machine learning ; Pattern recognition (psychology) ; Nuclear magnetic resonance ; Chemistry ; Physics ; Nuclear magnetic resonance spectroscopy Lanthanide and Transition Metal Complexes ; Electron Spin Resonance Studies ; Magnetism in coordination complexes
Authors: Xiangyu Song | Siyi Luo
Source: International Journal of Hydrogen Energy (article, 2025-09-22)
Field: Chemistry — Inorganic Chemistry

Machine learning + microwave pyrolysis: Optimal design for the collaborative preparation of hydrogen production with ceramsite Hydrogen production ; Hydrogen ; Yield (engineering) ; Outlier ; Microwave ; Biomass (ecology) ; Optimal design ; Water content Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Thermochemical Biomass Conversion Processes
Authors: N Prashanth | Keerthi Kumar N
Source: Journal of Environmental Nanotechnology (article, 2025-06-30)
Field: Materials Science — Polymers and Plastics

Development of Sustainable Okra Fiber-Nano-SiO2-reinforced Epoxy Composites: Mechanical Characterization and Machine Learning-based Prediction The transformation to environmentally friendly components in composite production is essential to minimize the environmental impact and expenditures. Substituting natural fibers, especially those from plants like okra, for conventional reinforcing elements in polymer composites makes them eco-friendlier and more cost-effective. This study aims to characterize and produce polymer composites reinforced with nano-SiO2 filler and chemically-treated okra fibers. Okra fibers, derived from the okra plant, stand out due to their accessibility, mechanical qualities, and positive impact on the environment. Physical and mechanical properties of composites, such as water absorption, tensile strength, and flexural strength, are studied concerning weight percentage, length, thickness, and nano-SiO2 content. Composites with fibers of 12 mm length, 2 mm thickness, and 8 wt.% nano-SiO2 arrangement have shown excellent performance. These composites have a flexural strength of 55.65 MPa and an ultimate tensile strength of 35.92 MPa, which enhances the structural integrity and longevity of the composites. Increasing the fiber weight percentage results in higher water absorption rates, reaching a peak of 3.26%, which highlights the significant impact of fiber dimensions. In addition, when comparing the performance of Response Surface Methodology (RSM) and Feed-Forward Artificial Neural Network (FFANN), predictive modeling using RSM yielded an average accuracy of 85-90%, whereas FFANN achieved 95-98%. Finally, the experimental results were validated through microstructural studies, which underscored the potential of okra fibers to enhance the performance of natural fiber-reinforced composites in various industrial applications. Epoxy ; Composite material ; Characterization (materials science) ; Nano- ; Materials science ; Fiber ; Nanotechnology Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Mechanical Engineering and Vibrations Research
Authors: A.E. Abd-elnaby | Khaled E. El-Kelany | F. Selim | Mostafa A. Ebied | A. M. Elshaer
Source: Journal of Energy Storage (article, 2025-10-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Evaluating machine learning models for predicting the storage performance of polypyrrole-based supercapacitor electrodes Mean squared error ; Gradient boosting ; Boosting (machine learning) ; Supercapacitor ; Cyclic voltammetry ; Dielectric spectroscopy ; Random forest ; Partial least squares regression ; Ensemble learning ; Mean absolute percentage error Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Yong Ju Lee | Soon Wan Kweon | Tai-Ju Lee | Hyoung Jin Kim
Source: International Journal of Biological Macromolecules (article, 2025-08-27)
Field: Materials Science — Biomaterials

Quantification of cellulose II using infrared spectroscopy: Machine learning approaches Cellulose ; Infrared spectroscopy ; Infrared ; Spectroscopy ; Polymer science ; Materials science ; Chemistry ; Computer science ; Organic chemistry ; Physics ; Optics Advanced Cellulose Research Studies ; Material Properties and Processing ; Cultural Heritage Materials Analysis
Authors: Jianjun Cai | Qianlang Liang | Ming Luo
Source: Chemical Engineering & Technology (article, 2025-06-09)
Field: Chemistry — Inorganic Chemistry

Synergistic Acceleration of Adsorbent Material Development by DFT and ML for CO<sub>2</sub> Capture Abstract The development of high‐performance solid adsorbents for CO 2 capture is crucial for reducing carbon emissions and combating climate change. Density functional theory (DFT) has been widely used to explore the adsorption mechanisms of solid adsorbents, but its computational cost limits large‐scale material screening. Machine learning (ML) as a data‐driven approach promotes materials development. This paper reviews the synergistic integration of DFT and ML in the design and development of solid amine adsorbents, metal‐organic framework materials, and calcium‐based adsorbents. With high‐quality training data generated by DFT, ML models can effectively predict material properties. In addition, the integration of ML accelerates high‐throughput screening, significantly improving the speed and accuracy of material discovery. This review summarizes recent advances and perspectives in the application of computational methods for the rational design of solid adsorbents. Adsorption ; Density functional theory ; Materials science ; Rational design ; Metal-organic framework ; Computer science ; Amine gas treating ; Process engineering ; Nanotechnology ; Chemistry ; Engineering ; Organic chemistry ; Computational chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Essam M. Elsaid | Sameh E. Ahmed | Zeinab Morsy | Reima Daher Alsemiry | Mohamed R. Eid
Source: Journal of Nonlinear Mathematical Physics (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Influence of Variable Lorentz Force-Dependent Viscosity on Mixed Double Diffusion in Inclined Finned Rectangular Containers: RSM Optimization and ANN Prediction This study examines the effect of a non-uniform magnetized field (variable Lorentz force) and mixed double diffusion within inclined rectangular containers. A wire carrying an electric current, located at a specific point near the lower edge, generates the variable Lorentz force. The dynamic viscosity of the ferrofluids (blood-Fe3O4) is supposed to change as a function of the non-uniform Lorentz force. The flow domain includes two heated fins of variable lengths, with the upper wall moving at a constant velocity. Key effects considered in this analysis include viscous dissipation and an exponential chemical reaction. The governing equations are derived based on ferrohydrodynamic (FHD) principles, transformed into dimensionless forms, and numerically solved using the control volume method (CVM) with a semi-implicit approach for pressure distributions. Additionally, advanced techniques such as response surface method (RSM) and artificial neural networks (ANN) are utilized to enhance and predict the heat transfer rate under varying control parameters. The results indicate that lower Rayleigh numbers lead to forced convection dominance, while lower Reynolds numbers result in wave-like heat transfer behavior characteristic of buoyancy-driven flow. Modifying the fin lengths was found to obstruct overall flow but enhance the temperature and concentration gradients. Furthermore, at a fixed fin length ( $$\:H=0.5$$ ), increasing the Reynolds number to 1000 enhances the heat transfer rate by up to 61%. Lorentz force ; Mathematics ; Diffusion ; Viscosity ; Variable (mathematics) ; Mechanics ; Lorentz transformation ; Response surface methodology ; Thermodynamics ; Classical mechanics ; Mathematical analysis ; Physics ; Statistics High voltage insulation and dielectric phenomena ; Nanofluid Flow and Heat Transfer ; Rheology and Fluid Dynamics Studies
Authors: Zhaohui Zhou | Yonghui Wang | Ximei Zhai
Source: Journal of Constructional Steel Research (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Thermomechanical response of Q355B steel under varying strain rates and temperatures: experiments and modeling In this study, uniaxial compression experiments were performed on Q355B steel to investigate its mechanical response under coupled loading of varying strain rates (0.0008–5000 s −1 ) and temperatures (25–900 °C). The plastic behaviors of the Q355B steel showed strong dependencies on temperature and strain rate. Abnormal mechanical responses of Q355B steel, marked by elevated flow stress at higher temperatures and lower strain rates, were also observed at certain loading scenarios due to dynamic strain aging (DSA). Additionally, the temperature associated with DSA shifted to higher value as the strain rate increases. A novel phenomenological constitutive model was proposed to characterize the thermomechanical responses of Q355B steel. Moreover, a three-hidden-layer artificial neural network (ANN) was first developed to predict the DSA-affected material behaviors over a wide range of strain rates. The prediction accuracy of the phenomenological and ANN models was quantitatively assessed using four statistical indicators: correlation coefficient, root mean square error, average absolute relative error, and relative error. The results demonstrated that both models achieved acceptable accuracy, and the ANN exhibited significantly superior predictive performance compared to the phenomenological constitutive model. • Compressive loading tests on Q355B steel were conducted at varied temperatures and strain rates. • Anomalous material behaviors induced by dynamic strain aging (DSA) were discussed. • A phenomenological model incorporating the DSA effect was proposed and calibrated. • An artificial neural network (ANN) was developed to improve the prediction accuracy of material behavior. • Two model performances were quantitatively compared using statistical indicators. Structural engineering ; Materials science ; Strain (injury) ; Strain rate ; Composite material ; Forensic engineering ; Engineering ; Medicine High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Microstructure and mechanical properties
Authors: Peng‐Hu Du | Qian Wang | Qiang Sun | Puru Jena
Source: Physical review. B./Physical review. B (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Thermal rectification of Sierpiński tetrahedron fractals assembled from supertetrahedral <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>-type tin selenide clusters To explore the potential of atomic clusters in device applications, we illustrate the thermal rectification (TR) effect and underlying mechanisms in the cluster-assembled Sierpi\ifmmode \acute{n}\else \'{n}\fi{}ski tetrahedron (ST) fractals. Using the synthesized supertetrahedral ${T}_{2}$-type ${\mathrm{Sn}}_{4}{\mathrm{Se}}_{10}$ clusters as building blocks and nonequilibrium molecular dynamics with a machine-learning neuroevolution potential trained on a multidimensional ab initio dataset, we show that the TR coefficient of the assembled ${\mathrm{ST}}_{4}$ fractal reaches 17.4% at an average temperature of 300 K with a temperature bias of 20 K. This further increases to 39.4% under the same temperature condition for the higher-level fractal ${\mathrm{ST}}_{5}$ as the void ratio and surface roughness increase significantly with the fractal level. Moreover, unlike in conventional atom-based nanosystems, the dependence of the TR coefficient on temperature bias in the ST fractals is nonmonotonic, where a high TR efficiency can be achieved just with a small temperature bias. Our work demonstrates the merits of cluster-assembled fractals for high-performance thermal rectifiers and the advantages of cluster-assembled materials for thermal energy management. Sierpinski triangle ; Tetrahedron ; Fractal ; Computer science ; Mathematics ; Algorithm ; Artificial intelligence ; Combinatorics ; Computer graphics (images) ; Geometry ; Mathematical analysis Phase-change materials and chalcogenides ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Tiantian Luan | Liwen Sang | Ziling Cai | Yutao Fang | Yabing Li | Rui Yin | Jianlu Wang | Bo Shen
Source: Advanced Electronic Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Nano‐Scale Phonon Dynamics in GaN: Fundamentals and Strategies for Thermal Transport Modulation ABSTRACT With the rapid advancement of electronic technologies, the effective thermal management in gallium nitride (GaN) ‐based devices has emerged as a critical challenge, particularly as device dimensions shrink to scales comparable to phonon mean free paths. In this regime, phonon‐mediated heat transport is governed by size‐dependent phenomena, such as boundary scattering, lattice confinement, and interface mode mismatch, which fundamentally deviate from bulk behaviors. Progress remains hindered by an insufficient understanding of phonon dynamics, resolution limits of characterization, and the inadequate incorporation of these insights into thermal management strategies. This review addresses these gaps by dissecting phonon‐dominated thermal transport in dimensionally confined GaN structures and at its heterointerfaces from both theoretical investigations and experimental characterizations. It further highlights the recent progress in in situ vibrational electron energy‐loss spectroscopy for the atomic‐scale visualization of phonon modes. Furthermore, the interface engineering between GaN and its adjunction materials is reviewed with strategies to minimize the thermal boundary resistance. Finally, future directions emphasize the integration of multiscale simulations, in situ characterization of multiple interfaces, and machine learning to deepen the fundamental understanding and optimize nano‐scale phonon transport for next‐generation GaN electronic applications. Phonon ; Thermal ; Gallium nitride ; Characterization (materials science) ; Interface (matter) ; Lattice (music) ; Boundary value problem ; Thermal conductivity Thermal properties of materials ; GaN-based semiconductor devices and materials ; Advanced Thermoelectric Materials and Devices
Authors: Platon P. Chebotaev | Andrey A. Buglak
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting the quantum yield of <sup>1</sup> O <sub>2</sub> generation for pteridines and fluoroquinolones using machine learning Fluoroquinolones (FQs) are a family of antibiotic drugs well-known for their high photochemical activity: upon UV-vis excitation FQs may produce singlet oxygen and/or lose the fluorine atom. Pterins (Ptrs) are a class of organic photosensitizers with a quantum yield of singlet oxygen generation up to 47% even in the absence of heavy atoms (metals and halogens). The similarities between the electronic absorption spectra of FQs and Ptrs led us to examine their photochemistry in a single study: both FQs and Ptrs are aza-bicyclic compounds. In this paper, we describe and compare machine learning methods suitable for predicting the photosensitizing ability of FQs and Ptrs. We investigated the singlet oxygen generation quantum yield (<i>Φ</i><sub>Δ</sub>) for 48 pterins and fluoroquinolones in deuterated water. To build machine learning (ML) models, we selected a dataset containing more than 5000 molecular descriptors, including both quantum chemical and molecular graph theory derivatives, the number of which was reduced using a genetic algorithm (GA). For model development and refinement, we employed multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), gradient boosting (GBR) and extreme gradient boosting (XGBoost) techniques. The obtained models demonstrate high predictive performance (<i>R</i><sub>train</sub><sup>2</sup> > 0.97, <i>q</i><sup>2</sup> > 0.84), with SVR achieving the best results for the test set (<i>R</i><sub>test</sub><sup>2</sup> = 0.975), and XGBoost showing the best overall robustness. Interpretability analysis revealed that descriptor relevance varied across models. Descriptors such as conjugated maximum bond length (CMBL) and long-range polarizability (TDB09p) consistently emerged as key contributors to <i>Φ</i><sub>Δ</sub>. Interpretability analyses (SHAP and ALE) confirmed their mechanistic relevance, highlighting the complementary contributions of linear and nonlinear models. These results demonstrate the feasibility of unified modeling of structurally diverse photosensitizers and provide actionable insights for the rational design and virtual screening of new compounds for photodynamic therapy. Interpretability ; Singlet oxygen ; Support vector machine ; Boosting (machine learning) ; Test set ; Polarizability ; Quantum machine learning ; Molecular descriptor ; Quantum chemistry Machine Learning in Materials Science ; Photodynamic Therapy Research Studies ; Photoreceptor and optogenetics research
Authors: Yuhao Zhao | Liya Zhu | Yihang Li | Heng Pan | Ming Chen | Wenhao Li | Guozhi Jia | Yu‐Bin Chen | Youjun Lu
Source: Chemical Engineering Journal (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Data-driven discovery of thermo-mechanically compatible Mn-based air electrodes for high-performance reversible protonic ceramic cells Electrolyte ; Electrode ; Ceramic ; Power density ; Electrochemistry ; High-temperature electrolysis ; Electrolysis ; Thermal stability ; Electrolysis of water Advancements in Solid Oxide Fuel Cells ; Gas Sensing Nanomaterials and Sensors ; Catalytic Processes in Materials Science
Authors: Himmet Özarslan | İhsan Uluocak
Source: Fuel (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

CNN-LSTM and CNN-GRU based estimation of NOx conversion efficiency in diesel engine exhaust system Diesel engine ; NOx ; Diesel fuel ; Turbocharger ; Nitrogen oxide ; Bayesian optimization ; Particulates ; Reduction (mathematics) Catalytic Processes in Materials Science ; Air Quality Monitoring and Forecasting ; Vehicle emissions and performance