jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nguyen Thi Hoang Phuong | Phan Minh Nhật | Nguyen Van Hieu
Source: JUCS - Journal of Universal Computer Science (article, 2025-12-22)
Field: Chemistry — Analytical Chemistry

NirMACNet: A Novel Multi-Scale Adaptive Convolutional Network for NIR Spectroscopy Near-infrared (NIR) spectroscopy has emerged as a valuable analytical technique for assessing the composition and quality of various materials. This study proposes NirMACNet, a novel convolutional neural network (CNN) architecture that incorporates a residual-based multi-scale kernel mechanism for enhanced prediction of compositional attributes. The model is evaluated on two distinct NIR spectral datasets, milk and soil, to demonstrate its generalization capability across domains. By leveraging multiscale kernel operations, NirMACNet effectively captures diverse spectral patterns, while its deep architecture facilitates comprehensive feature extraction. To mitigate performance degradation commonly associated with deeper networks, residual learning is employed. Experimental results indicate that NirMACNet consistently outperforms state-of-the-art methods in terms of prediction accuracy. Future work will involve expanding the diversity of training datasets and investigating alternative architectural enhancements to further improve model robustness and applicability. Robustness (evolution) ; Convolutional neural network ; Residual ; Kernel (algebra) ; Pattern recognition (psychology) ; Generalization ; Artificial neural network ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote-Sensing Image Classification
Authors: S. Kim | Jungho Kim
Source: ArXiv.org (preprint, 2025-07-10)
Field: Materials Science — Materials Chemistry

Interpretable inverse design of optical multilayer thin films based on extended neural adjoint and regression activation mapping We propose an extended neural adjoint (ENA) framework, which meets six key criteria for artificial intelligence-assisted inverse design of optical multilayer thin films (OMTs): accuracy, efficiency, diversity, scalability, flexibility, and interpretability. To enhance the scalability of the existing neural adjoint method, we present a novel forward neural network architecture for OMTs and introduce a material loss function into the existing neural adjoint loss function, facilitating the exploration of material configurations of OMTs. Furthermore, we present the detailed formulation of the regression activation mapping for the presented forward neural network architecture (F-RAM), a feature visualization method aimed at improving interpretability. We validated the efficacy of the material loss by conducting an ablation study, where each component of the loss function is systematically removed and evaluated. The results indicated that the inclusion of the material loss significantly improves accuracy and diversity. To substantiate the performance of the ENA-based inverse design, we compared it against the residual network-based global optimization network (Res-GLOnet). The ENA yielded the OMT solutions of an inverse design with higher accuracy and better diversity compared to the Res-GLOnet. To demonstrate the interpretability, we applied F-RAM to diverse OMT structures with similar optical properties, obtained by the proposed ENA method. We showed that distributions of feature importance for various OMT structures exhibiting analogous optical properties are consistent, despite variations in material configurations, layer number, and thicknesses. Furthermore, we demonstrate the flexibility of the ENA method by restricting the initial layer of OMTs to SiO2 and 100 nm. Artificial neural network ; Inverse ; Flexibility (engineering) ; Function (biology) ; Inverse problem ; Feature (linguistics) ; Activation function ; Layer (electronics) Phase-change materials and chalcogenides ; Magneto-Optical Properties and Applications ; Laser Material Processing Techniques
Authors: Fuqiang Xue | Feng Xue
Source: Engineering Reports (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

A Lifecycle‐Aware Optimization Framework for Low‐Carbon Material Selection in Engineering Product Design ABSTRACT Achieving carbon neutrality in engineering design requires effective emission reduction strategies throughout the entire product lifecycle. Material selection is a key element in this process, as it spans multiple stages and has a decisive impact on total carbon emissions. However, existing material selection methods often rely on static rules or single‐stage assessments, lacking adaptability to complex process design scenarios and the ability to capture cross‐stage interactions. As a result, they fall short of meeting the dynamic and accuracy requirements of real‐world applications. To address this, we propose a lifecycle‐oriented framework, Low‐Carbon Industrial Design Material Selection (LIDMS), for intelligent material selection in engineering applications. The framework integrates a deep learning‐based prediction module, the Multi‐Branch Fusion Module (MFM), with a constraint‐aware combinatorial optimization engine. The MFM captures complex relationships between material properties and design parameters across four lifecycle stages using a stage‐aware attention mechanism. These predictions are embedded into a differentiable optimization model that identifies material combinations minimizing total lifecycle emissions while meeting engineering constraints such as strength, cost, and recyclability. Experimental validation using synthetic data and a real‐world case study (a handheld electric sander casing) demonstrates the method's performance and practical applicability in reducing emissions under realistic constraints. The proposed LIDMS framework offers a scalable solution for sustainable material decision‐making in industrial product and process design. Our code is available at https://github.com/XueFuqiang/Low‐Carbon‐Industrial‐Design‐Material‐Selection.git . Adaptability ; Engineering design process ; Material selection ; Selection (genetic algorithm) ; Process (computing) ; Scalability ; Product (mathematics) ; Engineering optimization Machine Learning in Materials Science ; Material Selection and Properties ; Sustainable Supply Chain Management
Authors: Eman M. Zayed | A. I. Alateyah | Waleed H. El‐Garaihy | Mahmoud Shaban | Majed Fedhi Alinizzi | Nahed A. El‐Mahallawy
Source: MetalMat (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Advancing Severe Plastic Deformation for Tubular Samples: Systematic Review and Machine Learning Optimization ABSTRACT Severe Plastic Deformation (SPD) is a widely recognized method for producing ultrafine‐grained (UFG) and nano‐structured materials with superior mechanical properties by imposing high strains without the need for alloying elements or secondary reinforcements. Although SPD techniques for bulk and sheet materials have been extensively developed, recent advancements have focused on creating effective SPD methods specifically designed for tubular samples. These techniques are critical for industries where lightweight, high‐strength tubular components are essential, such as aerospace, automotive, and biomedical sectors. Machine learning (ML) approaches have emerged as powerful tools for optimizing SPD parameters and predicting material behavior. ML involves constructing systems capable of analyzing and identifying patterns in data to make informed decisions, such as supervised and unsupervised learning, which analyze training data to reveal relationships and trends. When dealing with small datasets, cross‐validation (CV), particularly k‐fold CV, is a reliable technique to prevent overfitting and enhance model robustness. ML and statistical analysis were employed to predict and optimize the input parameters in terms of the plastic strain (PS) and the impact on the improvement of the mechanical properties. This review provides a comprehensive analysis of SPD processes tailored for tubular materials, including methods such as High‐Pressure Tube Twisting (HPTT), Accumulative Spin Bonding (ASB), and Parallel Tubular Channel Angular Pressing (PTCAP). Each method is examined in terms of deformation behavior, die design, and microstructural evolution. Additionally, the review addresses critical research gaps, explores potential industrial applications, and highlights how advancements in ML can enhance the understanding and application of SPD techniques, offering insights into scaling up these processes for large‐scale production. Overfitting ; Deformation (meteorology) ; Severe plastic deformation ; Pressing ; Experimental data Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Nonlocal and gradient elasticity in micro/nano structures
Authors: John G. Pavek (17093350) | Nicholas E. Bollis (21518884) | Josiah Grimes (20764691) | Michael R. Shortreed (200226) | Lloyd M. Smith (200274) | Michael T. Marty (7822148)
Source: Figshare (article, 2025-06-10)
Field: Chemistry — Spectroscopy

A Fast Neural Networkfor Isotopic Charge State Assignment Electrospray ionization (ESI) mass spectrometry is an essential technique for chemical analysis in a range of fields. In ESI, analytes can produce multiple charge states, which must be correctly assigned for identification. Existing approaches to charge state assignment can suffer from limited accuracy or poor speed. Here, we developed a fast neural network to perform isotopic envelope charge assignment. The performance of our algorithm, IsoDec, was demonstrated on top-down proteomics spectra collected on diverse instruments. On these highly complex individual spectra, we found that IsoDec correctly assigns more features compared to existing software tools while simultaneously providing improved speed and accuracy. Importantly, this performance enhancement stems directly from the neural network charge assignment approach and not simply from improved scoring and filtering of isotopic envelopes. Finally, when applied to large top-down proteomics data sets, we discovered that database searching of the IsoDec deconvolution output produces proteoform-spectrum matches with a better combination of coverage and accuracy. Overall, IsoDec provides a compelling demonstration of the potential of lightweight neural networks in mass spectrometry data analysis for diverse applications. Deconvolution ; Artificial neural network ; Range (aeronautics) ; Mass spectrometry ; Charge (physics) ; Software ; State of charge Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jiazheng Zhao | Shengen Zhang | Jian Wang | Jun Zhang | Jun Li | Jingge Dai | Fenghua Luo
Source: JOM (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Accelerating Steel Design with Multi-objective Machine Learning Gaussian process ; Robustness (evolution) ; Inverse ; Pareto principle ; Generalization ; Inverse problem ; Process (computing) ; Covariance ; Engineering design process ; Uncertainty quantification Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Model Reduction and Neural Networks
Authors: Farhan Sheth | Priya Mathur | Hammad Shaikh | Dheeraj Kumar | S. R. Mishra | A. K. Gupta
Source: International Communications in Heat and Mass Transfer (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Learning heat: High-fidelity experimental and Koo–Kleinstreuer–Li thermal conductivity predictions in nanofluids via advanced data augmentation and metaheuristic search The precise forecasting of thermal conductivity in nanofluids is essential for enhancement of thermal management systems within industrial contexts. This research establishes a robust framework that combines machine learning (ML), deep learning (DL), advanced augmentation, and optimization strategies to predict two types of thermal conductivity: experimental thermal conductivity (Exp-TC) and the effective thermal conductivity based on the Koo-Kleinstreuer-Li model (KKL-TC). The study curates a comprehensive dataset consisting of 278 samples, which includes a variety of nanoparticle materials (such as Cu , CuO , Al , and Al 2 O 3 ) and base fluids (including water , ethylene glycol , and transformer oil ). Critical parameters, including particle size, temperature, volume fraction, thermal conductivity of particle and liquid medium, were enhanced through advanced data augmentation techniques—specifically, polynomial and fourier expansion inspired augmentation and conditional variational autoencoders to improve data diversity and the robustness of the models. The approach utilized fourteen ML and DL models in two configurations: standalone and ensemble stacking with linear regression. Optimization techniques, such as grey wolf optimization and particle swarm optimization, were employed to refine hyperparameters and enhance predictive accuracy. The results reveal that both augmentation and optimization significantly boost predictive accuracy. Specifically, CatBoost delivers the best Exp-TC (R 2 = 0.99964, RMSE = 0.00464) and KKL-TC performance (R 2 = 0.99782, RMSE = 0.00391). These insights establish a highly scalable and adaptable framework that extends beyond thermal system design to a broad range of engineering and industrial applications. This framework drives innovation in energy management, facilitating the development of more efficient, sustainable, and intelligent thermal systems. Nanofluid ; Thermal conductivity ; Particle swarm optimization ; Mean squared error ; Robustness (evolution) ; Data point ; Artificial neural network ; Thermal Thermal properties of materials ; Nanofluid Flow and Heat Transfer ; Power Transformer Diagnostics and Insulation
Authors: Kulko, Roman-David | Hanus, Andreas | Elser, Benedikt
Source:  (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Generative Artificial Intelligence for Synthetic Spectral Data Augmentation in Sensor-Based Plastic Recycling The reliance on deep learning models for sensor-based material classification amplifies the demand for labeled training data. However, acquiring large-scale, annotated spectral data for applications such as near-infrared (NIR) reflectance spectroscopy in plastic sorting remains a significant challenge due to high acquisition costs and environmental variability. This paper investigates the potential of large language models (LLMs) in synthetic spectral data generation. Specifically, it examines whether LLMs have acquired sufficient implicit knowledge to assist in generating spectral data and introduce meaningful variations that enhance model performance when used for data augmentation. Classification accuracy is reported exclusively as a proxy for structural plausibility of the augmented spectra; maximizing augmentation performance itself is not the study’s goal. From as little as one empirical mean spectrum per class, LLM-guided simulation produced data that enabled up to 86% accuracy, evidence that the generated variation preserves class-distinguishing information. While the approach performs best for spectral distinct polymers, overlapping classes remain challenging. Additionally, the transfer of optimized augmentation parameters to unseen classes indicates potential for generalization across material types. While plastic sorting serves as a case study, the methodology may be applicable to other domains such as agriculture or food quality assessment, where spectral data are limited. The study outlines a novel path toward scalable, AI-supported data augmentation in spectroscopy-based classification systems. Sorting ; Synthetic data ; Generalization ; Data quality ; Hyperspectral imaging ; Generative model ; Raw data ; Artificial neural network Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Water Quality Monitoring and Analysis
Authors: PECORELLA, FRANCESCO
Source: Electronic Theses and Dissertations Repository (University of Pisa) (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

A Machine Learning Framework to describe the solid and liquid phases in Germanium Telluride Germanium Telluride (GeTe) is one of the simplest phase-change materials (PCMs), a class of compounds able to reversibly switch between crystalline and disordered phases with drastic changes in electrical and optical properties. Despite its simple stoichiometry, GeTe exhibits remarkable behavior, making it the prototypical system to study the fundamental mechanisms of PCMs, which are central to nonvolatile memory technologies. Understanding these transformations requires largescale atomistic simulations, but the cost of density functional theory (DFT) limits their scope, while classical molecular dynamics (MD) relies on accurate interatomic potentials. In this work, we develop a machine learning interatomic potential (MLP) for ground-state GeTe. After detailed convergence studies, a DFT dataset of crystalline and liquid configurations is generated to train the chosen model. The MLP is then validated against DFT and tested in MD simulations, showing reliable performance for both phases. This framework enables accurate and efficient simulations of GeTe, paving the way for large scale studies of PCM behavior relevant to memory applications. Germanium ; Telluride ; Germanium compounds ; Density functional theory ; Convergence (economics) ; Interatomic potential ; Molecular dynamics ; Simple (philosophy) Machine Learning in Materials Science ; Phase-change materials and chalcogenides ; 2D Materials and Applications
Authors: Zhikang Tian | Xiaotong Liu | Bei Zhang | Zhedong Ge | Yucheng Zhou
Source: BioResources (article, 2025-12-15)
Field: Chemistry — Organic Chemistry

A classification method of softwood species for building and interior decoration based on deep learning The material properties of softwood species affect the safety of building structures, and wood identification is a key factor in material certification in specific institutions for green building certification. This study investigated an efficient wood species identification algorithm, aiming to provide a reliable method for material selection in construction and decoration industries. Using microscopic cross-sectional images of 36 softwood species applied in construction and decoration as research objects, 11 classic deep learning models were employed for species classification, combined with class activation map analysis to examine the key structural features for species identification. Specifically, the model structure and advantages of Swin Transformer were highlighted, in which hierarchical feature extraction and shifted window attention mechanism enable multi-scale fusion of wood structural features, such as tracheids, within global contexts, thereby improving classification accuracy for wood cross-sectional images. Experimental results showed that the Swin Transformer model achieved the highest classification accuracy of 99.97%, with both precision and recall exceeding 99% and an F1 score of 99%. These findings validate that deep learning networks based on the Transformer framework can achieve reliable image classification performance in wood research. Softwood ; Deep learning ; Transformer ; Pattern recognition (psychology) ; Feature extraction ; Certification Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Zhikang Tian | Xiaotong Liu | Bei Zhang | Zhedong Ge | Yucheng Zhou
Source: SHILAP Revista de lepidopterología (article, 2025-12-01)
Field: Chemistry — Organic Chemistry

A Classification Method of Softwood Species for Building and Interior Decoration Based on Deep Learning The material properties of softwood species affect the safety of building structures, and wood identification is a key factor in material certification in specific institutions for green building certification. This study investigated an efficient wood species identification algorithm, aiming to provide a reliable method for material selection in construction and decoration industries. Using microscopic cross-sectional images of 36 softwood species applied in construction and decoration as research objects, 11 classic deep learning models were employed for species classification, combined with class activation map analysis to examine the key structural features for species identification. Specifically, the model structure and advantages of Swin Transformer were highlighted, in which hierarchical feature extraction and shifted window attention mechanism enable multi-scale fusion of wood structural features, such as tracheids, within global contexts, thereby improving classification accuracy for wood cross-sectional images. Experimental results showed that the Swin Transformer model achieved the highest classification accuracy of 99.97%, with both precision and recall exceeding 99% and an F1 score of 99%. These findings validate that deep learning networks based on the Transformer framework can achieve reliable image classification performance in wood research. Softwood ; Deep learning ; Transformer ; Pattern recognition (psychology) ; Feature extraction ; Certification Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Wei E. I. Sha | Tienchong Chang
Source: ArXiv.org (preprint, 2025-12-02)
Field: Materials Science — Materials Chemistry

Modeling and Inverse Identification of Interfacial Heat Conduction in Finite Layer and Semi-Infinite Substrate Systems via a Physics-Guided Neural Framework Heat transfer in semiconductor devices is dominated by chip and substrate assemblies, where heat generated within a finite chip layer dissipates into a semi-infinite substrate with much higher thermophysical properties. This mismatch produces steep interfacial temperature gradients, making the transient thermal response highly sensitive to the interface. Conventional numerical solvers require excessive discretization to resolve these dynamics, while physics-informed neural networks (PINNs) often exhibit unstable convergence and loss of physical consistency near the material interface. To address these challenges, we introduce HeatTransFormer, a physics-guided Transformer architecture for interface-dominated diffusion problems. The framework integrates physically informed spatiotemporal sampling, a Laplace-based activation emulating analytical diffusion solutions, and a mask-free attention mechanism supporting bidirectional spatiotemporal coupling. These components enable the model to resolve steep gradients, maintain physical consistency, and remain stable where PINNs typically fail. HeatTransFormer produces coherent temperature fields across the interface when applied to a finite layer and semi-infinite substrate configuration. Coupled with a physics-constrained inverse strategy, it further enables reliable identification of three unknown thermal properties simultaneously using only external measurements. Overall, this work demonstrates that physics-guided Transformer architectures provide a unified framework for forward and inverse modeling in interface-dominated thermal systems. Thermal conduction ; Discretization ; Heat transfer ; Inverse ; Finite element method ; Thermal ; Chip ; Thermal conductivity ; Semiconductor device ; Substrate (aquarium) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Thermal properties of materials
Authors: Rui Cao | Liping Feng | Xiaohu Yu | Yulong Yang | Lingfeng Jia | Yuan Tian | Yunjia Huang
Source: Applied Materials Today (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Growth of p-type BiOBr via machine learning-assisted LPCVD and Its application on BiOBr/WS2 p-n junction diode with high rectification ratio Rectification ; Chemical vapor deposition ; Diode ; Fabrication ; Photodetection ; Electrical junction 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Chalcogenide Semiconductor Thin Films