jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hajime Yano | A.A. Yamamura | Shigeyuki Sakane | Tomohiro Takaki
Source: IOP Conference Series Materials Science and Engineering (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Estimation of interface properties from dendrite images using systematic phase-field simulations and convolutional neural network Abstract The absence of quantitative material data poses a major challenge in phase-field (PF) simulations of dendrite growth. In this study, a convolutional neural network (CNN) model was applied to estimate interface properties from 2D cross-sectional images of dendritic microstructures. The training dataset was generated through systematic 2D PF simulations of dendrite growth during the directional solidification of an Al-3wt%Cu alloy, with variations in both the anisotropy strength of the interface energy and the preferred growth direction. The results show that an increase in the amount of training data improves the estimation accuracy of interface property estimations. However, a lack of similarity between the training and test dendrite images significantly reduces the accuracy. In addition, estimation errors increase when the preferred growth direction deviated from the temperature gradient, particularly when using a CNN model trained only on dendrite morphologies aligned with the temperature gradient. These findings highlight the importance of aligning training conditions with experimental data and suggest that pre-training with diverse datasets can improve estimation accuracy. In future work, we plan to apply the CNN model to 2D cross-sections obtained from 3D structures. Dendrite (mathematics) ; Convolutional neural network ; Interface (matter) ; Artificial neural network ; Similarity (geometry) ; Pattern recognition (psychology) ; Anisotropy Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Aluminum Alloy Microstructure Properties
Authors: Andrea N. Giordano (1272015) | Samuel Franqui-Rios (21444813) | Steven M. Quarin (21444816) | Der Vang (14824546) | Drake R. Austin (21444819) | Abigail G. Doyle (1318335) | Luke A. Baldwin (1525828) | Pietro Strobbia (6324983) | Rahul Rao (649857)
Source: Figshare (article, 2025-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of SERSSubstrates Using Silver-CoatedGold Nanostars for Chemical Sensing: A Multiobjective Bayesian OptimizationApproach Field detection of trace analytes remains a challenge in many sectors, including national security, public health, and environmental monitoring. This challenge arises from the scarcity of field-deployable technologies capable of identifying trace analytes in the complex matrices of real-world samples. Surface-enhanced Raman scattering (SERS) is a promising technique for chemical sensing because it offers sensitive and specific analyte detection with commercial portable Raman systems. Despite the immense potential of SERS, commercially available SERS technologies that are accurate, reliable, cost-effective, and compatible with portable spectrometers remain scarce. This scarcity is due to the challenges associated with the complex design and resource-intensive optimization of the manufactured substrates that can achieve both high SERS intensity and signal uniformity. Machine learning (ML) tools are ideal for addressing complex optimization problems, but their application in SERS substrate fabrication remains largely unexplored. We present a pioneering example of applying ML to optimize SERS substrate fabrication using silver-coated gold nanostars for chemical sensing. In this study, we used multiobjective Bayesian optimization to achieve both high SERS intensity and signal uniformity across the SERS substrate. Optimal fabrication parameters were identified for spin and flow coating of silver-coated gold nanostars. The optimized spin coated substrate achieved an experimental limit of detection of 100 nM with an application relevant compound (ammonium nitrate), suggesting its potential for trace-level sensing. This work underscores the potential for ML to accelerate the development of field-deployable SERS technologies for trace analyte detection. Furthermore, we provide a general framework for optimized fabrication of nanoparticles on surfaces for other applications such as catalysis, electronics, and medicine. Fabrication ; Analyte ; Substrate (aquarium) ; Bayesian optimization ; Raman scattering ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zheng Yin | Zhiyang Jiang | Zhong-Ke Jiang | Lijuan Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Carbon Dioxide Adsorption Performance of Covalent Organic Frameworks (COFs) Based on Machine Learning: Identification of Key Factors and Practical Applications Adsorption ; Key (lock) ; Identification (biology) ; Interface (matter) ; Greenhouse gas ; Covalent bond Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Florent Purnode | Louis Denis | François Henrotte | Gilles Louppe | Christophe Geuzaine
Source: Open Repository and Bibliography (University of Liège) (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accounting for Hysteresis and Eddy Currents in FEM Simulations of Ferromagnetic Laminated Cores using a Recurrent Neural Network Accounting for hysteresis and eddy currents in finite element simulations of electrical machines with ferromagnetic laminated cores is expensive. These phenomena have how- ever a significant impact on the machine performance. To account for them with a low computational cost, we propose to train a recurrent neural network to serve as material law in 2D finite element simulations. Finite element method ; Hysteresis ; Eddy current ; Ferromagnetism ; Artificial neural network ; Magnetic hysteresis ; Preisach model of hysteresis Magnetic Properties and Applications ; Electromagnetic Simulation and Numerical Methods ; Electric Motor Design and Analysis
Authors: Mingsheng Cai
Source: Highlights in Science Engineering and Technology (article, 2025-07-29)
Field: Chemistry — Analytical Chemistry

Nondestructive Measurement of Soluble Solid Content in Plums Using a Multispectral Camera: A Methodological Study Soluble Solid Content (SSC) is a key parameter for assessing fruit quality, reflecting sweetness and nutritional value. Traditional methods for SSC measurement, such as chemical assays, require destructive sampling, limiting their applicability to large-scale quality monitoring. This study presents a nondestructive SSC detection method combining multispectral imaging, hybrid machine learning (RF-PLS), and physics-based optical modeling. Optical data of plum samples were acquired using a multispectral camera (350–950 nm). Noise reduction was performed via Savitzky-Golay filtering and SNV transformation, followed by wavelength selection using SPA and RF-FI scoring. By integrating the Kubelka-Munk theory to decode light propagation in fruit flesh, we established a hybrid model that synergizes physical insights with machine learning. The RF-PLS framework achieved superior accuracy (R²=0.89, RMSE=2.17%) over standalone algorithms, validating the power of multimodal fusion in SSC prediction. Multispectral image ; Content (measure theory) ; Remote sensing ; Environmental science ; Materials science ; Mathematics ; Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Maqsood, Ayman | Näsström, Hampus | Chen, Chen | Qiutong, Li | Luo, Jingshan | Chakraborty, Rayan | Blum, Volker | Unger, Eva | Draxl, Claudia | Márquez, José A. | Jacobsson, T. Jesper
Source: edoc Publication server (Humboldt University of Berlin) (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Towards an interoperable perovskite description or how to keep track of 300 perovskite ions Hybrid perovskites are interesting optoelectronic materials. The perovskite ABX3 structure offers a vast compositional space, and we have identified over 300 perovskite ions. This flexibility enables tuneable properties and has significantly contributed to the success of perovskite optoelectronics. However, this diversity also leads to confusion, ambiguity, and inconsistencies causing challenges for data mining and machine learning applications. To address this issue, we propose guidelines and a JSON schema to standardize the reporting of perovskite compositions. The schema adheres to IUPAC recommendations and is designed to make data both human- and machine-readable. It captures key descriptors such as perovskite composition, molecular formula, SMILES representation, IUPAC name, and CAS number for each ion. To facilitate adoption, we have developed utilities to automatically generate comprehensive and standardized perovskite descriptions from standard ion abbreviations and stoichiometric coefficients. Additionally, we provide a curated database of all identified perovskite ions with associated descriptive data. Perovskite (structure) ; Interoperability ; Schema (genetic algorithms) ; Chemical nomenclature ; Ion ; Key (lock) ; Stoichiometry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Jesper Byggmästar | Ville-Markus Yli-Suutala | Aslak Fellman | Jan Åström | Jan Westerholm | Fredric Granberg
Source: ArXiv.org (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Four regimes of primary radiation damage in tungsten We observe for the first time in silico the transition to a linear regime in the primary damage production in tungsten. As the critical plasma-facing material in fusion reactors, radiation damage in tungsten has been studied extensively in experiments and simulations. Irradiation experiments routinely produce recoils in the MeV range while full atomistic modelling has been limited to a few hundred keV. Here we bridge these scales with extremely large-scale and accurate machine-learning-driven molecular dynamics simulations with recoil energies up to 2 MeV in systems up to one billion atoms. We reveal four regimes of primary damage as a function of damage energy, with a transition to a high-energy regime that deviates from all previous models. Curiously, the start of the high-energy regime coincides with the highest possible recoil energy to tungsten atoms from fusion-emitted neutrons (300 keV). Radiation damage ; Tungsten ; Recoil ; Irradiation ; Neutron ; Radiation Nuclear materials and radiation effects ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Ravi Kiran Bollineni | Zhifei Deng | Michael S. Kesler | Michael Tonks | Ling Li | Sachin Kurian
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework Finite element method ; Pearlite ; Microstructure ; Genetic algorithm ; Deep learning ; Inverse ; Optimal design ; Multiscale modeling ; Ferrite (magnet) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Yang Yu | Chang Ding | Jinwei Chen | Huilin Mu | Fan‐Yi Meng | Jiafu Wang
Source: IEEE Transactions on Microwave Theory and Techniques (article, 2025-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DeepMeasurement: Complex Permittivity Characterization of Liquid Crystals Using Transfer Learning Liquid crystal (LC) is a kind of anisotropic material with tunable permittivity, giving it significant application in the microwave field. Therefore, accurate characterization of the complex permittivity of LC materials at microwave frequency is of great importance for LC-based tunable microwave devices. In this article, a Gaussian process regression (GPR)-based deep learning network model, named DeepMeasurement, is generated and trained using transfer learning, which can be used to characterize the complex permittivity of LCs from transmission parameters. First, to simply and efficiently obtain the transmission response of the LC material under test (MUT), a measurement setup based on artificial coaxial structure has been designed and fabricated. In addition, to train the initial network parameters of GPR-based DeepMeasurement, large data of the source domain is rapidly generated by a simple multi layer model and transmission matrix theory. Furthermore, the transfer learning is performed by small data of the target domain generated by full-wave simulation to calibrate the model parameters. The introduction of transfer learning has effectively improved the accuracy of measurement results and the efficiency of network parameter generation. Finally, one prototype is fabricated and measured, respectively. Experimental results demonstrate that the DeepMeasurement can effectively establish a mapping from transmission parameters to the complex permittivity of LC-MUT, and the relative permittivity and loss tangent values can be accurately extracted, thereby proving the effectiveness of the proposed method. Permittivity ; Materials science ; Characterization (materials science) ; Relative permittivity ; Dielectric ; Optoelectronics ; Electronic engineering ; Engineering ; Nanotechnology Liquid Crystal Research Advancements
Authors: Syakir Hakimi Zainulabidin | Suriani Mat Jusoh | Samsuri Abdullah
Source: Revue des composites et des matériaux avancés (article, 2025-08-31)
Field: Materials Science — Polymers and Plastics

Application of Artificial Neural Network for Parameters Optimization on Tensile Properties via Hand Lay-up Techniques for Kenaf/Fibreglass Reinforced Hybrid Composite The growing demand for sustainable, high-performance materials has led to the development of hybrid composites that integrate natural and synthetic fibers.This study explores the tensile properties of kenaf/fiberglass reinforced polyester hybrid composites fabricated through the hand lay-up technique.Composites incorporating varying kenaf fiber weight percentages (15%, 45%, 60%, and 75%) were evaluated in accordance with American Society for Testing and Materials (ASTM) D3039 standards.Additionally, an Artificial Neural Network (ANN) model was constructed to predict tensile strength based on fiber content, composite thickness, and defect levels.The model was trained using three different algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG).The composite with 45% kenaf fiber demonstrated the optimal tensile performance.The highest measured tensile strength reached 50.47 MPa.The ANN model achieved a high prediction accuracy with a correlation coefficient (R) of 0.9686 and a Mean Squared Error (MSE) of 0.0063.Among the training algorithms, the LM algorithm outperformed the others in terms of prediction accuracy.These findings highlight the effectiveness of ANN modelling in optimizing hybrid composite formulations, minimizing experimental requirements, and advancing their use in structural applications. Ultimate tensile strength ; Composite number ; Artificial neural network ; Kenaf ; Conjugate gradient method ; Fiber ; Natural fiber ; Correlation coefficient Natural Fiber Reinforced Composites ; BIM and Construction Integration ; Mechanical Behavior of Composites
Authors: Sami Shibah
Source: Research Square (preprint, 2025-09-12)
Field: Chemistry — Inorganic Chemistry

A Multi-Scale Theoretical Framework for Quantum-Enhanced Carbon Capture via Zn-Porphyrin Metal-Organic Frameworks Density functional theory ; Work (physics) ; Blueprint ; Stability (learning theory) ; Reduction (mathematics) ; Ab initio ; Carbon fibers ; Efficient energy use Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: A. V. Motseyko | S. A. Guda | D. Mukherjee | R. G. Burkovsky | N. V. Ter-Oganessian
Source: Physical review. B./Physical review. B (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Emerging challenges from molecular dynamics calculations of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>PbZrO</mml:mi> <mml:mn>3</mml:mn> </mml:msub> </mml:math> and their implications for the correct understanding of phase transitions and the electrocaloric effect Lead zirconate, ${\mathrm{PbZrO}}_{3}$ (PZO), is a perovskite crystal with an exceptional diversity of possible low-symmetry structures. Its phase transitions that are driven by temperature, field, pressure, and epitaxy are therefore of intense interest from experimental and theoretical viewpoints. Those viewpoints do not yet match in many important aspects. We conduct machine-learning-based molecular dynamics (MD) simulations to investigate several critical properties of PZO and identify recurring experiment--theory inconsistencies that often go underreported. We show that (i) MD correctly reproduces the experimental decoupling of antipolar displacements and octahedral rotations under pressure; (ii) MD predicts a positive electrocaloric effect, in contrast to experimentally observed negative values; and (iii) electric-field-induced transitions in MD proceed from $Pbam$ to $R3c$, whereas experiments suggest a transition to $R3m$. These findings reveal challenging limitations in current MD models---particularly in capturing entropy-driven phase stability---that must be addressed to reconcile simulations with experimental behavior in PZO. Phase transition ; Molecular dynamics ; Decoupling (probability) ; Perovskite (structure) ; Phase (matter) ; Crystal (programming language) ; Granularity Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Thermal Expansion and Ionic Conductivity
Authors: Fatih Uzun | Alexander M. Korsunsky
Source: Journal of Intelligent Manufacturing (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Evolutionary artificial intelligence for 3D microstructure reconstruction based on inverse pole figure images Abstract Reconstructing three-dimensional grain structures from planar imaging data remains a central challenge in materials science, as real microstructures are inherently three-dimensional while most characterization techniques provide only two-dimensional maps. This study presents an artificial intelligence-driven framework for reconstructing three-dimensional polycrystalline microstructures from a single inverse pole figure image. A genetic algorithm, guided by a fitness function based on the distance of statistical descriptors, is employed to evolve grain structures that match the reference microstructure without requiring large training datasets or deep learning architectures. The method is validated on both synthetic data and an experimental inverse pole figure map from an Inconel 718H superalloy. Results show high fidelity, with grain size distribution peaks in the synthetic case deviating by less than 0.2 units from the reference. A parameter sensitivity analysis confirms the robustness of the approach. The proposed method provides a computationally efficient, data-light alternative for microstructure reconstruction, offering direct relevance to materials design, digital twin development, and structure–property modeling workflows. Robustness (evolution) ; Inverse ; Inverse problem ; Microstructure ; Pattern recognition (psychology) ; Synthetic data ; Genetic algorithm Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Model Reduction and Neural Networks
Authors: Weijun Xing | Xuquan Wang | Zhiyuan Ma | Yujie Xing | Xiong Dun | Xinbin Cheng
Source: Optics (article, 2025-10-13)
Field: Chemistry — Analytical Chemistry

Rapid Discrimination of Platycodonis radix Geographical Origins Using Hyperspectral Imaging and Deep Learning Platycodonis radix is a commonly used traditional Chinese medicine (TCM) material. Its bioactive compounds and medicinal value are closely related to its geographical origin. The internal components of Platycodonis radix from different origins are different due to the influence of environmental factors such as soil and climate. These differences can affect the medicinal value. Therefore, accurate identification of Platycodonis radix origin is crucial for drug safety and scientific research. Traditional methods of identification of TCM materials, such as morphological identification and physicochemical analysis, cannot meet the efficiency requirements. Although emerging technologies such as computer vision and spectroscopy can achieve rapid detection, their accuracy in identifying the origin of Platycodonis radix is limited when relying solely on RGB images or spectral features. To solve this problem, we aim to develop a rapid, non-destructive, and accurate method for origin identification of Platycodonis radix using hyperspectral imaging (HSI) combined with deep learning. We captured hyperspectral images of Platycodonis radix slices in 400–1000 nm range, and proposed a deep learning classification model based on these images. Our model uses one-dimensional (1D) convolution kernels to extract spectral features and two-dimensional (2D) convolution kernels to extract spatial features, fully utilizing the hyperspectral data. The average accuracy has reached 96.2%, significantly better than that of 49.0% based on RGB images and 81.8% based on spectral features in 400–1000 nm range. Furthermore, based on hyperspectral images, our model’s accuracy is 14.6%, 8.4%, and 9.6% higher than the variants of VGG, ResNet, and GoogLeNet, respectively. These results not only demonstrate the advantages of HSI in identifying the origin of Platycodonis radix, but also demonstrate the advantages of combining 1D convolution and 2D convolution in hyperspectral image classification. Hyperspectral imaging ; Pattern recognition (psychology) ; RGB color model ; Convolution (computer science) ; Deep learning ; Identification (biology) ; Radix (gastropod) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research