jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meihui Liu | Wei Wang | Peifeng Su | Jiajun Ren | Qian Peng
Source: The Journal of Physical Chemistry Letters (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

A Molecular Descriptor for Characterizing the Strength of Through-Space Conjugation for Luminescent Property in Organic Materials Noncovalent through-space conjugation (TSC), a unique property of π-charge delocalization, has emerged as a powerful strategy for modulating the optoelectronic properties of organic materials. However, the quantitative relationship among the molecular structure, the strength of TSC, and optoelectronic properties has not yet been established. Here, using typical conjugated aromatic compounds as model systems, it is revealed that TSC arises from the interaction of π-orbitals, and a molecular descriptor, composed of geometric parameters, is proposed to characterize the strength of TSC. This descriptor exhibits a strong linear correlation with the orbital splitting energy of symmetric and antisymmetric π-orbitals, achieving a linear fit coefficient (<i>R</i><sup>2</sup>) as high as 0.935. More importantly, <i>A</i> shows a strong positive correlation with the spectral red-shift of organic materials, suggesting that TSC can effectively tune the luminescent properties of organic materials. The descriptor is helpful for the rational design and machine-learning-based high-throughput screening of advanced organic luminescent materials. Property (philosophy) ; Luminescence ; Space (punctuation) ; Materials science ; Nanotechnology ; Computer science ; Optoelectronics ; Philosophy ; Epistemology Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Synthesis and Properties of Aromatic Compounds
Authors: Chih‐Wei Chu | Chao-Hsin Lu | Wing Yiu Yeung
Source: Langmuir (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Systematic Evaluation of Atrous Spatial Pyramid Pooling in U-Net for Pore Segmentation in Plasma Electrolytic Oxidation Coatings Plasma Electrolytic Oxidation (PEO) coatings enhance the physical and chemical properties of metallic substrates, including corrosion resistance, wear resistance, and thermal stability. These enhancements are strongly influenced by the porous surface morphology of the coatings, which affects the ion transport, stress distribution, and permeability. Accurate quantification of pore structures is essential for understanding interfacial structure-property relationships, yet traditional image segmentation methods often fail to capture the complexity of PEO surfaces in SEM images. This study presents a deep learning-based segmentation framework using U-Net architectures integrated with Atrous Spatial Pyramid Pooling (ASPP) to improve multiscale feature extraction. The performance impact of ASPP placement within different parts of U-Net was systematically evaluated. Results show that modifications to the bridge and decoder paths have the greatest impact on segmentation performance, with a combined modification applying ASPP in both achieving the highest F1 score (0.9360) and the highest IoU (0.8798). Statistical analysis using 5-fold cross-validation, bootstrap confidence intervals, and paired <i>t</i>-tests confirmed that only the bridge-modified model (<i>B</i><sub>1×1</sub>) significantly outperformed the baseline (<i>p</i> < 0.05). The proposed approach enables high-fidelity pore segmentation and supports advanced microstructural analysis of PEO coatings. By facilitating accurate morphological quantification, it contributes to the understanding of structure-property relationships in interfacial materials and offers a robust tool for future materials characterization workflows. Pooling ; Materials science ; Pyramid (geometry) ; Segmentation ; Porosity ; Computer science ; Artificial intelligence ; Composite material ; Chemical engineering ; Physics ; Optics ; Engineering Corrosion Behavior and Inhibition ; Infrastructure Maintenance and Monitoring ; Advanced Neural Network Applications
Authors: Zhibo Zhang | Yani Wang | Mengzhen Zhu | Zhenhua Zhao | Lingling Lv | Haochen Zhu | Xingong Zhang | Xin Zhou | Hao Yan | Chaohe Yang | Xiaobo Chen
Source: Computers & Chemical Engineering (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Integrating machine learning and life cycle sustainability assessment for systematic optimization of petroleum coke oxidation for hydrogen residues processing Sustainability ; Life-cycle assessment ; Coke ; Petroleum coke ; Petroleum ; Process engineering ; Waste management ; Hydrogen ; Environmental science ; Chemical engineering ; Computer science ; Engineering ; Biochemical engineering ; Chemistry ; Manufacturing engineering ; Organic chemistry ; Production (economics) ; Economics Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Iron and Steelmaking Processes
Authors: Imtiaz Qavi | George Z. Tan
Source: Polymer (article, 2025-06-17)
Field: Materials Science — Biomaterials

Modeling the morphology of collagen-loaded sodium alginate-polyethylene oxide composite electrospun fibers using interpretable machine learning Composite number ; Polyethylene oxide ; Materials science ; Composite material ; Morphology (biology) ; Sodium alginate ; Electrospinning ; Polyethylene ; Sodium ; Polymer Electrospun Nanofibers in Biomedical Applications ; Cardiac and Coronary Surgery Techniques ; Tissue Engineering and Regenerative Medicine
Authors: Yuxin Zhan | Xiaojie Ren | Shuai Zhao | Zhanglin Guo
Source: Materials Today Energy (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Atomic-number-informed encoding descriptor to predict the stability of perovskite oxides and halides Halide ; Materials science ; Perovskite (structure) ; Stability (learning theory) ; Encoding (memory) ; Inorganic chemistry ; Chemical engineering ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Mingyu Yang | Dongni Han | Chaokun Huang | Yanlong Luo | Xiujuan Wang
Source: Materials Today Communications (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Enhancing the performance and mechanism of NR/SBR composites by silane coupling agents: An experimental and machine learning study Materials science ; Composite material ; Coupling (piping) ; Silane ; Mechanism (biology) Boron and Carbon Nanomaterials Research ; Conducting polymers and applications ; Polymer Nanocomposites and Properties
Authors: Xinyi Liu | Y. K. Heng | Liping Zhou | Han Gao | Yanyan Ji | Wu Zhang
Source: International Journal of Hydrogen Energy (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Optimizing methane catalytic hydrogen production via a hybrid Boruta-XGB and stacking ensemble machine learning framework Hydrogen production ; Stacking ; Process (computing) ; Ensemble learning ; Production (economics) ; Feature (linguistics) ; Hydrogen Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Zezhu Zeng | Zheyong Fan | Michele Simoncelli | Chen Chen | Ting Liang | Yue Chen | G. Thornton | Bingqing Cheng
Source: Proceedings of the National Academy of Sciences (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Lattice distortion leads to glassy thermal transport in crystalline Cs <sub>3</sub> Bi <sub>2</sub> I <sub>6</sub> Cl <sub>3</sub> The glassy thermal conductivities observed in crystalline inorganic perovskites such as Cs<sub>3</sub>Bi<sub>2</sub>I<sub>6</sub>Cl<sub>3</sub> are perplexing and lacking theoretical explanations. Here, we first experimentally measure its thermal transport behavior from 20 to 300 K, after synthesizing Cs<sub>3</sub>Bi<sub>2</sub>I<sub>6</sub>Cl<sub>3</sub> single crystals. Using path-integral molecular dynamics simulations driven by machine learning potentials, we reveal that Cs<sub>3</sub>Bi<sub>2</sub>I<sub>6</sub>Cl<sub>3</sub> has large lattice distortions at low temperatures, which may be related to the large atomic size mismatch. Employing the Wigner formulation of thermal transport, we reproduce the experimental thermal conductivities based on lattice-distorted structures. This study thus provides a framework for predicting and understanding glassy thermal transport in materials with strong lattice disorder. Thermal ; Lattice (music) ; Molecular dynamics ; Thermal conductivity ; Measure (data warehouse) ; Thermal conduction Advanced Thermoelectric Materials and Devices ; Optical properties and cooling technologies in crystalline materials ; Phase-change materials and chalcogenides
Authors: Shaozhe Ding | Longbin Liu | Shifeng Zhang | Mingkun Li
Source: Scientific Reports (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

An LSTM-driven thermoelectric coupling response prediction method for shape memory alloy actuators Shape memory alloys (SMAs) show exceptional potential in actuator design due to their shape memory effect and superelasticity, yet their thermoelectric hysteresis challenges accurate modeling. This study proposes a hybrid framework integrating long short-term memory (LSTM) networks with physical kinematics to predict SMA actuator responses. Unlike conventional approaches, our method decouples material behavior prediction from actuator geometry: A single-layer LSTM network processes voltage-time sequences to predict SMA wire's temperature and resistance dynamics, while a physics-based model computes angular displacement through phase transformation and constitutive equations. Trained on 10 experimental conditions (1-2 °C/s heating rates) and tested on 3 unseen cases (0.8 °C/s), the model achieves a mean absolute error of < 5% in angular displacement prediction, with root mean square errors of 2.5 × 10<sup>-5</sup> for temperature/resistance outputs. The modular architecture eliminates neural network retraining during structural iterations-only ordinary differential equation parameters require adjustment. This approach advances rapid actuator optimization, demonstrating faster computational efficiency compared to full-physics models. Our findings establish a computationally sustainable paradigm for smart material-based actuation systems, extensible to piezoelectric and magnetostrictive materials through constitutive parameter substitution. Shape-memory alloy ; Coupling (piping) ; Computer science ; Alloy ; Thermoelectric effect ; Actuator ; Materials science ; Artificial intelligence ; Physics ; Composite material ; Thermodynamics Shape Memory Alloy Transformations
Authors: Winni Schwedland | Tatiana C. Penna | Henning Windeck | Fabian Müller | Stephen Leach | Joachim Sauer | Xavier R. Advincula | Fabian Berger | Naoyuki Ishida | Keijiro Ohshimo | Fuminori Misaizu | Ya-Ke Li | Arghya Chakraborty | Francine Horn | Knut R. Asmis
Source: Chemical Science (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Unexpected structural isomers of AlFe<sub>2</sub>O<sub>4</sub><sup>+</sup> and AlCo<sub>2</sub>O<sub>4</sub><sup>+</sup>: vibrational spectroscopy and ion mobility combined with quantum chemistry The structure and reactivity of the mixed metal oxide clusters Al<sub>2</sub>MO<sub>4</sub> <sup>+</sup> and AlM<sub>2</sub>O<sub>4</sub> <sup>+</sup> (M = Fe, Co), formally obtained by transition metal ion substitution from Al<sub>3</sub>O<sub>4</sub> <sup>+</sup>, are studied using infrared photodissociation (IRPD) spectroscopy, ion-mobility mass-spectrometry (IM-MS) and quantum chemistry. We use density functional theory (DFT) in combination with global structure optimization to identify low energy structures and to connect them to the IRPD and IM-MS data. Insights into anharmonic and temperature effects are obtained from machine learning-based molecular dynamics simulations. While all metal ions are equal in the cone-shaped structure of M<sub>3</sub>O<sub>4</sub> <sup>+</sup>, the mixed metal oxide clusters attain different, more stable structures, in which the metal ions are either in different oxidation states (Al<sub>2</sub>MO<sub>4</sub> <sup>+</sup>) or have different coordination numbers (AlM<sub>2</sub>O<sub>4</sub> <sup>+</sup>). The present results illustrate that different DFT functionals may accurately describe local minimum structures, but reliable relative energies of isomers with differently coordinated transition metal ions require multi-reference wavefunction calculations. Spectroscopy ; Ion ; Infrared spectroscopy ; Crystallography ; Chemistry ; Materials science ; Physics Inorganic and Organometallic Chemistry ; Chemical Synthesis and Characterization ; Thermal and Kinetic Analysis
Authors: Ricardo Pool-Mazun | Salman A. Khan | Vinson Liao | Thomas K. Hansen | Md Raian Yousuf | Piaoping Yang | Abhijit Shrotri | Adam S. Hoffman | Simon R. Bare | Dionisios G. Vlachos | Ayman M. Karim
Source: Nanoscale Advances (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Reversible temperature-induced shape transition of Pt nanoparticles supported on Al <sub>2</sub> O <sub>3</sub> Supported platinum catalysts are widely used in industry for hydrogenation reactions. The variations of the electronic and geometric properties of Pt nanoparticles due to temperature can greatly affect their reactivity. In this work, we use <i>in situ</i> X-ray absorption spectroscopy and environmental transmission electron microscopy to study the effect of H<sub>2</sub> and temperature on the shape and electronic properties of 1.8 nm average diameter Pt nanoparticles supported on Al<sub>2</sub>O<sub>3</sub>. We utilize actively trained machine learning potentials with uniform acceptance force-bias Monte Carlo (fbMC) to estimate the structural distribution of Pt<sub>15</sub>/γ-Al<sub>2</sub>O<sub>3</sub> (110) clusters at finite temperatures. Our predicted cluster geometries are consistent with experimental data showing the nanoparticles reversibly change shape from 3D hemispheres at low temperatures (35-100 °C) to 2-2.5D rafts at higher temperatures (200-400 °C). Furthermore, experiments and computations indicate that the contraction in Pt-Pt bond distances and higher electron density on Pt at higher temperatures are attributed primarily to the change in nanoparticle shape and associated increased interaction with Al<sub>2</sub>O<sub>3</sub>. Our results show the fluxional nature of supported Pt nanoparticles driven by temperature changes. Nanoparticle ; Transmission electron microscopy ; Cluster (spacecraft) ; Platinum ; Platinum nanoparticles ; Monte Carlo method ; Absorption spectroscopy ; Spectroscopy ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Chieh Fu Hsiao | Georg Feyrer | Anthony Stein
Source: Smart Agricultural Technology (article, 2025-05-19)
Field: Chemistry — Analytical Chemistry

Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks Using convolutional neural networks (CNNs) to detect plant diseases has proven to reach high accuracy in the classification of infected and non-infected plant images. However, most of the existing researches are based on RGB images due to the availability and the comparably low cost of image collection. The limited spectral information restricts the detectability of plant diseases, especially in the early stage where often symptoms of pathogen infection have not yet become visible. To this end, in this study, hyperspectral images (HSI) are combined with deep learning models to test the classification ability of two soybean fungal diseases: Asian soybean rust ( Phakopsora pachyhizi ) and soybean stem rust ( Sclerotinia scleroriorum ). Different CNNs employing 2D, 3D convolution, and hybrid approaches are compared. The influences of the depth of the convolutional layer and the regularization techniques are also discussed. Besides, image augmentation methods are investigated to overcome the problem of data scarcity. The results indicate the 6-convolutional-layer depth hybrid model to have the best capacity in classifying Asian soybean rust in the early-mid to mid-late stage when there are over 2% visible symptoms but a limited detectability in the early stages when there are below 2% visible symptoms on leaves. On the other hand, the optimized CNN model shows a limited capability to detect both diseases when there are no visible symptoms observable. Overall, this study suggests a hybrid 2D-3D convolutional model with augmentation and regularization methods has a high potential in the early detection of fungal diseases. This research is expected to contribute to a new cropping system that vastly reduces the chemical-synthesis plant protection products, where a continuous pathogen disease monitoring plays a key to manage the crop stands. Hyperspectral imaging ; Convolutional neural network ; Stage (stratigraphy) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Remote sensing ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Lingjun Zeng | Yu Zhang | Lan Xie | Bai Xue | Qiang Zheng
Source: Carbon (article, 2025-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Asymmetric multistage composite foams for high-efficient absorption-dominant electromagnetic interference shielding exploited with aid of machine learning EMI ; Electromagnetic shielding ; Electromagnetic interference ; Composite number ; Reflection coefficient ; Electrical conductor ; Absorption (acoustics) ; Interference (communication) Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Fields and Biological Effects
Authors: Kazuma Ito | Takashi Otaki | Katsutoshi Hyodo | Tatsuya Yokoi | Hideki Mori
Source: Research Square (preprint, 2025-07-18)
Field: Materials Science — Metals and Alloys

Machine-learning potential reveals the origin of hydrogen embrittlement at general grain boundaries in α-Fe Hydrogen embrittlement ; Embrittlement ; Grain boundary ; Hydrogen ; Materials science ; Dislocation ; Cracking ; Deformation (meteorology) ; Plasticity ; Chemical physics ; Metallurgy ; Chemistry ; Microstructure ; Composite material ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Xiaolong Sun | Zhijian Li | Xiao Qin | Xinquan Xu | Zhisheng Liu
Source: Journal of Materials Engineering and Performance (article, 2025-10-15)
Field: Materials Science — Polymers and Plastics

Physicochemical Properties and Microstructural Failure Mechanisms of Finely Ground Elastomer Modifiers at Glass Transition Temperature Microstructure ; Scanning electron microscope ; Characterization (materials science) ; Elastomer ; Particle size ; Particle (ecology) ; Fourier transform infrared spectroscopy ; Amorphous solid Polymer Nanocomposites and Properties ; Structural Analysis of Composite Materials ; Material Properties and Applications
Authors: Aaron Flötotto | Benjamin Spetzler | Rose von Stackelberg | Martin Ziegler | Erich Runge | Christian Dreßler
Source: ArXiv.org (preprint, 2025-08-19)
Field: Materials Science — Materials Chemistry

Large-scale cooperative sulfur vacancy dynamics in two-dimensional MoS2 from machine learning interatomic potentials The formation of extended sulfur vacancies in MoS2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale molecular dynamics simulations using machine learning interatomic potentials (MLIPs) reveal key mechanisms of cooperative vacancy transport, including incorporation of vacancies into clusters of arbitrary size. The simulations provide a coherent atomistic explanation for irradiation-induced vacancy patterns observed experimentally, especially the formation of line defects spanning tens of nanometers. Results and performance are compared of two MLIP frameworks: (i) on-the-fly learning with Gaussian approximation potential, and (ii) fine-tuning of an equivariant foundation model. Vacancy defect ; Molecular dynamics ; Sulfur ; Interatomic potential ; Line (geometry) ; Gaussian Machine Learning in Materials Science ; 2D Materials and Applications ; Fuel Cells and Related Materials
Authors: Yongbo Shi | Yuanxu Zhu | Zihao Song | Ye Su | Ping Qian
Source: Physical review. B./Physical review. B (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Formation of native defects in boron arsenide and their impact on thermal conductivity It is predicted by the Boltzmann transport equation (BTE) that the thermal conductivity of boron arsenide (BAs) at room temperature is as high as 1441 W/(m K). However, the experimental measurements exhibit striking variations, ranging from 53 to 2100 W/(m K). To reveal the divergence, high-precision neuroevolution potential (NEP) is first trained using density-functional theory (DFT) data and machine learning methods. Subsequently, NEP-based molecular dynamics (MD) simulations are used to investigate the growth mechanism of BAs, the formation and recombination of native defects, and lattice thermal conductivity. At 900 K, the formation enthalpy and cohesive energy for the cubic phase BAs are \ensuremath{-}0.017 and \ensuremath{-}5.38 eV, indicating that vapor B and As atoms can form the BAs phase instead of precipitating elemental phases B and As. At high temperatures, ${\mathrm{V}}_{\mathrm{B}}$, ${\mathrm{V}}_{\mathrm{As}}$, ${\mathrm{A}}_{\mathrm{B}}$, and ${\mathrm{A}}_{\mathrm{As}}$ exhibit low formation energy, suggesting that they may exist during crystal growth. Furthermore, we observe that ${\mathrm{A}}_{\mathrm{B}}\text{\ensuremath{-}}{\mathrm{A}}_{\mathrm{As}}$ pairs undergo recombination. Our investigation suggests that the ${\mathrm{A}}_{\mathrm{B}}\text{\ensuremath{-}}{\mathrm{A}}_{\mathrm{As}}$ pairs can be eliminated by reducing the cooling rate of the crystal, which provides guidance for crystal growth. Applying the homogeneous nonequilibrium molecular dynamics (HNEMD) approach, the thermal conductivity for defect-free BAs is predicted to be 1333 W/(m K). Due to defects scattering phonons, the thermal conductivity is suppressed to 44--208 W/(m K) for a system with a defect concentration of 1.40 $\ifmmode\times\else\texttimes\fi{}{10}^{20}\phantom{\rule{0.28em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}3}$, which explains the underestimation of thermal conductivity in experimental measurements. This work establishes a pathway for identifying and understanding the functionality of defect suppression materials. Thermal conductivity ; Molecular dynamics ; Boron ; Enthalpy ; Boltzmann equation ; Conductivity ; Crystal (programming language) ; Thermal Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Wai Ho Reo Sze | Bin Xi | Junyi Zhu
Source: Computational Condensed Matter (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Key difference of input data organization to the predictions of symmetry information and layer number for quasi-2D films from band structure Symmetry (geometry) ; Key (lock) ; Layer (electronics) ; Statistical physics ; Materials science ; Electronic band structure ; Condensed matter physics ; Physics ; Computer science ; Mathematics ; Nanotechnology ; Geometry Graphene research and applications ; 2D Materials and Applications ; Advanced Semiconductor Detectors and Materials