jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuhao Chen | Shouqin Tian | J. G. Li | Yaju Zhang | Wenbo Peng | Zhongkun Wang | Gang Cheng | Yuanzheng Zhang | Guozhen Shen | Haiwu Zheng
Source: Nano Energy (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Performance enhancement of NbOI2-based field-effect transistor by piezotronic effect for object recognition Materials science ; Field-effect transistor ; Transistor ; Optoelectronics ; Field effect ; Object (grammar) ; Nanotechnology ; Electrical engineering ; Artificial intelligence ; Computer science ; Voltage ; Engineering 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Perovskite Materials and Applications
Authors: Yong Ju Lee | Do Young Lee | Tai-Ju Lee | Hyoung-Jin Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Cellulose I Crystallinity Estimation Using a Combination of Infrared Spectroscopy and Machine Learning Approaches Crystallinity ; Cellulose ; Infrared spectroscopy ; Spectroscopy ; Infrared ; Materials science ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Chemistry ; Chemical engineering ; Composite material ; Engineering ; Physics ; Optics ; Chromatography ; Organic chemistry Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Lignin and Wood Chemistry
Authors: Wang Wang (262236) | Kun Song (522456) | Shuqi Zou (9764774) | Qinwei Xia (21366529) | Xiaobai Li (152726) | Ke Sun (542534) | Hongwei Ma (290896)
Source: Figshare (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Long-Lifetime andMulticolor Room-Temperature Phosphorescenceof Carbon Quantum Dots Derived from Biomass: Applications on ConvolutionalNeural Network Material longevity and diversity of a broad color gamut undoubtedly play a role as rigid criteria for dynamic information encryption technologies, particularly for those based on room-temperature phosphorescence (RTP). As a typical example, high-security multidimensional discriminative encryption still faces several obstacles brought about from the intrinsic limitations of carbon nitride quantum dots (CNQDs) which should be overcome, e.g. the quenching susceptibility of triplet excitons, limited tunability of colorimetric emission, and metal-doping-induced instability. To achieve tunable room-temperature phosphorescence (RTP) for advanced information encryption, biofriendly CNQDs were synthesized in this work, in terms of boron-mediated lattice defect engineering and hydrogen-bond rigidification, while dual-color RTP emissions were successfully realized with exceptional afterglow durations, lasting 7 s at 503 nm as well as 2 s at 617 nm. Based on emission mechanisms dominated by n-π* transitions, distinct phosphorescent lifetimes were detected from red and cyan-green CNQDs, within 176 and 290 ms, respectively, and then temporal determinants and Rubik’s Cube matrix transformations strategies were established following with their temporal-spectral characteristics and finally applied to convolutional neural networks (CNN). In fact, this work can be regarded as an attempt to provide a creative model for designing sustainable phosphorescent materials with programmable optical properties, devoted to promoting next-generation information encryption technologies. Phosphorescence ; Quantum dot ; Graphitic carbon nitride ; Convolutional neural network ; Encryption ; Quenching (fluorescence) ; Phosphor ; Quantum Carbon and Quantum Dots Applications ; Luminescence and Fluorescent Materials ; Quantum Dots Synthesis And Properties
Authors: Yuhong Qiu | Wanping Chen | Lixin Ma | Chenxi Huang
Source: Coordination Chemistry Reviews (article, 2025-12-30)
Field: Chemistry — Inorganic Chemistry

AI-guided Design of Metal-Organic Frameworks and Integrated Sensing Platforms for Food Safety Intelligent Detection Food safety ; Multiplex ; Microfluidics ; Sensitivity (control systems) ; Intelligent decision support system Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gas Sensing Nanomaterials and Sensors
Authors: Yumin Dong | Bin Xu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Machine Learning-Driven Design and Optimization of Al₂O₃/SiC Composites for Enhanced Mechanical Properties Flexural strength ; Fracture toughness ; Vickers hardness test ; Sintering ; Toughness ; Key (lock) ; Particle swarm optimization Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; MXene and MAX Phase Materials
Authors: XYZ, ABC
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

ML-Surrogate Model This repository contains attachments related to the publication:**[<b>Machine Learning Surrogate Optimization Framework for an Additively Manufactured, Linearly Polarized Metal Coaxial-to-Circular Waveguide Transition for Space Applications</b>] ****Published in: ** [Engineering Applications of AI], [Volume xxx], [Issue yyy], [Year 2025], [Page Numbers zzz or DOI https://doi.org/10.1080/xxxyyyzzzz]## DescriptionThis repository provides access to additional resources that enhance or support the findings presented in the main publication. These materials may include,* **Datasets: ** The raw or processed datasets used for the analysis in the publication.***Surrogate Model: ** Models generated## Contents1-Folder Dataset &amp; Models ----------------------This folder has the Dataset and Trained Models## How to UseThe files in this repository are organized. You can browse the contents and download individual files as needed.To access the main publication, please refer to: the publication's DOI link https://doi.org/10.1080/xxxyyyzzzz].## LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.## CitationIf you use the data or materials in this repository, please cite the original publication. And if you specifically use any of the attached files, you may also consider referencing the repository.## ContactFor any questions or inquiries regarding this repository or the publication, please contact:<br>**Last Updated:** April 02, 2025 Download ; Space (punctuation) ; Set (abstract data type) ; Raw data ; Key (lock) ; Work (physics) ; Data access Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Aviraj M. Teli | Sonali A. Beknalkar | Ninad B. Velhal | Rutuja U. Amate | Pritam J. Morankar | Rushikesh P. Dhavale | Tukaram D. Dongale | Ho-Sung Kim | Jae Cheol Shin
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Boosting Energy Storage Performance via Electron Pathway Modulation in Hierarchical NiCo Oxide-Sulphide Hybrids Electrode ; Energy storage ; Supercapacitor ; Electrochemical energy storage ; Capacitance ; Electrochemistry ; Redox ; Nanostructure ; Flexibility (engineering) Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Barlo, Alexander
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Towards Sustainable and Intelligent Manufacturing Processes : Data-Driven Insights from Automotive Manufacturing Global manufacturing is entering an era of unprecedented variability in material properties, driven by sustainability goals and market volatility. The adoption of recycled steels introduces supplier-specific differences, while cost-reduction strategies and geopolitical disruptions (tariffs, trade barriers, and resource shortages) further amplify process scatter. These dynamics challenge conventional quality control in automotive sheet metal forming and demand intelligent, adaptable production systems. This dissertation addresses how data-driven methods can strengthen process robustness without major infrastructure changes. Three guiding hypotheses are explored: (H1) machine learning can provide insights into the impact of input variations; (H2) synthetic data can supplement or replace operational data for model development; and (H3) existing sensor signals can be reinterpreted to reduce reliance on additional instrumentation. A hybrid methodology combining finite element simulations, stochastic modeling, and industrial press shop data was developed. Key contributions include: (1) generation of synthetic datasets for predictive modeling of draw-in and cushion force; (2) application of unsupervised learning for early detection of anomalous material batches; and (3) a novel process monitoring metric, process work, derived from existing sensors to monitor process health. The findings provide a framework for integrating intelligent data-driven tools into legacy systems, supporting the transition toward resilient and sustainable manufacturing practices. Automotive industry ; Robustness (evolution) ; Process (computing) ; Sustainability ; Process control ; Process modeling ; Production (economics) ; Key (lock) ; Resource (disambiguation) ; Manufacturing execution system Machine Learning in Materials Science ; Digital Transformation in Industry ; Fault Detection and Control Systems
Authors: HASAN, MUHAMMAD FAHAD
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

ML-Surrogate Model This repository contains attachments related to the publication:**[<b>Machine Learning Surrogate Optimization Framework for an Additively Manufactured, Linearly Polarized Metal Coaxial-to-Circular Waveguide Transition for Space Applications</b>] ****Published in: ** [Engineering Applications of AI], [Volume xxx], [Issue yyy], [Year 2025], [Page Numbers zzz or DOI https://doi.org/10.1080/xxxyyyzzzz]## DescriptionThis repository provides access to additional resources that enhance or support the findings presented in the main publication. These materials may include,* **Datasets: ** The raw or processed datasets used for the analysis in the publication.***Surrogate Model: ** Models generated## Contents1-Folder Dataset &amp; Models ----------------------This folder has the Dataset and Trained Models## How to UseThe files in this repository are organized. You can browse the contents and download individual files as needed.To access the main publication, please refer to: the publication's DOI link https://doi.org/10.1080/xxxyyyzzzz].## LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/.## CitationIf you use the data or materials in this repository, please cite the original publication. And if you specifically use any of the attached files, you may also consider referencing the repository.## ContactFor any questions or inquiries regarding this repository or the publication, please contact:<br>**Last Updated:** December 14, 2025 Download ; Space (punctuation) ; Set (abstract data type) ; Raw data ; Key (lock) ; Work (physics) ; Data access Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Multi-Objective Optimization Algorithms
Authors: Lawrence A. Harris | Md Jayed Hossain | Ming-hua Qui | Ruichen Zhang | Pingchuan Ma | Tianlong Chen | Jiaqi Gu | Sefaattin Tongay | Umberto Celano
Source:  (preprint, 2025-09-17)
Field: Materials Science — Materials Chemistry

Sparse C-AFM: a deep learning method for fast and accurate characterization of MoS2 with conductive atomic force microscopy The increasing use of two-dimensional (2D) materials in nanoelectronics demands robust metrology techniques for electrical characterization, especially for large-scale production. While atomic force microscopy (AFM) techniques like conductive AFM (C-AFM) offer high accuracy, they suffer from slow data acquisition speeds due to the raster scanning process. To address this, we introduce SparseC-AFM, a deep learning model that rapidly and accurately reconstructs conductivity maps of 2D materials like MoS$_2$ from sparse C-AFM scans. Our approach is robust across various scanning modes, substrates, and experimental conditions. We report a comparison between (a) classic flow implementation, where a high pixel density C-AFM image (e.g., 15 minutes to collect) is manually parsed to extract relevant material parameters, and (b) our SparseC-AFM method, which achieves the same operation using data that requires substantially less acquisition time (e.g., under 5 minutes). SparseC-AFM enables efficient extraction of critical material parameters in MoS$_2$, including film coverage, defect density, and identification of crystalline island boundaries, edges, and cracks. We achieve over 11x reduction in acquisition time compared to manual extraction from a full-resolution C-AFM image. Moreover, we demonstrate that our model-predicted samples exhibit remarkably similar electrical properties to full-resolution data gathered using classic-flow scanning. This work represents a significant step toward translating AI-assisted 2D material characterization from laboratory research to industrial fabrication. Code and model weights are available at github.com/UNITES-Lab/sparse-cafm. Characterization (materials science) ; Data acquisition ; Deep learning ; Metrology ; Artificial neural network ; Electrical conductor ; Nanoelectronics ; Pixel ; Feature extraction ; Convolutional neural network Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Fuel Cells and Related Materials
Authors: Zixin Jin | Zhiwei Liu | Ziwuzhen Wang | Yuqing Yang | Hongyu Shi | Caixu Fan | Yamin Liu | Qiuyue Ji | Wei Sheng | Long Ma | Shuo Wang
Source: Chemical Engineering Journal (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Real-time food freshness monitoring with AI assisted colorimetric and fluorescent dual-mode paper-based sensors: Harnessing size-dependent effects of mesoporous Cu2O Food freshness assessment constitutes a critical metric for quality control, where timely detection of spoilage indicators is essential for ensuring food quality. Volatile amines (VAs) are key components constituting total volatile basic nitrogen (TVB-N), which are crucial indicators for assessing food freshness and spoilage. This study reports the synthesis of mesoporous Cu 2 O with controlled pore sizes (60, 140, 240 nm), followed by in situ reduction of platinum nanoparticles to construct high-performance nanozymes (P60-Cu 2 O@Pt, P140-Cu 2 O@Pt, P240-Cu 2 O@Pt). Systematic investigation reveals a specific surface area size-dependent enhancement in catalytic activity, with PS140-based nanozymes demonstrating optimal performance. A dual-mode sensing platform is developed by integrating P140-Cu 2 O@Pt with CdSe/ZnS quantum dots (QDs) on paper substrates. Through pH-mediated coordination with amine compounds releasing during protein decomposition, the sensor achieves simultaneous colorimetric and fluorescent detection of VAs, with limits of detection (LODs) reaching 0.834 mg/L (colorimetric) and 0.906 mg/L (fluorescent). Furthermore, a convolutional neural network (CNN)-based regression model is implemented for real-time data analysis, enabling quantitative spoilage prediction with 99 % accuracy. This eco-friendly, low-cost sensor exhibits exceptional adaptability to storage environments, with potential for integration into smart packaging systems. The technology provides consumers and suppliers with immediate freshness feedback, thereby reducing food waste and preventing foodborne illnesses. • Mesoporous Cu 2 O with pore sizes (60, 140, 240 nm) are used for nanozyme construction. • P140-Cu 2 O@Pt nanozymes show best catalytic activity. • Dual mode paper-based sensor can detect VAs via colorimetric and fluorescent. • CNN model can enable 99 % accurate real-time spoilage prediction. • Eco-friendly and low-cost smart packaging materials can reduce waste & illness risks. Food spoilage ; Mesoporous material ; Food industry ; Fluorescence ; Reduction (mathematics) ; Convolutional neural network ; Food safety ; Catalysis Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Electrochemical sensors and biosensors
Authors: Patyukova, Elena | Yin, Junwen | Basak, Susmita | Sanchez, Samuel Pinilla | Elena, Alin | Teobaldi, Gilberto
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Automatic generation of input files with optimised k-point meshes for Quantum Espresso self-consistent field single point total energy calculations Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results. This represents a particularly important problem for high-throughput and agentic workflows, where due to computational cost, any additional convergence studies are preferably to be avoided. So, there is a need for tools and models which are able to predict DFT parameters from basic input information, such as a structure. In this work, we develop a machine learning approach to predict the appropriate k-point sampling in DFT calculations and generate the input files for Quantum Espresso self-consistent field calculations. To achieve this, we first generated a training dataset comprising over 20,000 materials, each with an energy convergence threshold of 1 meV/atom. Several ML models were evaluated for their ability to predict k-points distance, and uncertainty estimation was incorporated to guarantee that, for at least 85-95% of compounds, the predicted k-distance lies within the convergence region. The best-performing models are made publicly available through an open-access web application. Convergence (economics) ; Field (mathematics) ; Single point ; Energy (signal processing) ; Point (geometry) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Matthew Thoms | Sun, Hao | Laurent Karim Béland
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Benchmarking 34 OpenKIM Nickel Potentials with an Emphasis on Surfaces and Extended Defects We present an automated benchmarking suite for face-centered-cubic (FCC) nickel that evaluates 47 quantitative metrics spanning both standard tests (equation of state, elastic constants, surface energies and phonons) and application-specific scenarios such as defect formation and migration, grain boundaries, step edges, close-range interactions, and vacancy cluster energetics. Using this framework, we assess 34 interatomic potentials from the OpenKIM repository, including pairwise, embedded-atom, modified-embedded-atom, angular-dependent, and spectral neighbor analysis potentials (SNAP). Results are compared against ab initio benchmarks compiled from the literature. Most potentials accurately reproduce lattice parameters, elastic constants, and surface energies, whereas predictive accuracy degrades for migration barriers and short-range compression. Principal-component analysis identifies correlated property groups and a partially orthogonal component associated with migration and short-range physics, revealing Pareto trade-offs between accuracy domains. SNAP models occupy the lowest-error frontier, although several embedded-atom potentials remain competitive across many metrics. The framework provides a reproducible baseline for potential selection, highlights systematic limitations across formalisms, and supports benchmarking-in-the-loop strategies for developing next-generation machine-learning potentials for Ni and Ni-based alloys. Benchmarking ; Interatomic potential ; Suite ; Nickel ; Ab initio ; Lattice (music) ; Surface (topology) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Tarik Sadat
Source: Applied Physics A (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

A machine learning approach to predicting resonance frequency in soft magnetic composites Gradient boosting ; Sintering ; Mean squared error ; Boosting (machine learning) ; Random forest ; Robustness (evolution) ; Resonance (particle physics) ; Magnetic resonance imaging Magnesium Oxide Properties and Applications ; Metallic Glasses and Amorphous Alloys ; Aluminum Alloys Composites Properties
Authors: Tahsinul Huq | Yew Hoong Wong | Joon Huang Chuah | Chee‐Keong Tan | Shuye Zhang
Source: Ultramicroscopy (article, 2025-09-08)
Field: Materials Science — Surfaces, Coatings and Films

Novel technique for aluminum thin film thickness measurement using top view SEM-EDX in conjunction with electron beam simulation and machine learning Thin film ; SIGNAL (programming language) ; Substrate (aquarium) ; Dispersion (optics) ; Aluminium ; Beam (structure) ; Consistency (knowledge bases) ; Cathode ray Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Semiconductor materials and devices
Authors: Harshit Sharma | Gaurav Arora | Raj Kumar | Suman Debnath | Suchart Siengchin
Source: Iranian Polymer Journal (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Data-driven insights into the high-temperature behavior of polymer/carbon nanotubes nanocomposites Rockwell scale ; Ultimate tensile strength ; Composite number ; Nanocomposite ; Tensile testing ; Random forest ; Gradient boosting ; Boosting (machine learning) Carbon Nanotubes in Composites ; Polymer crystallization and properties ; Polymer Nanocomposites and Properties
Authors: Sungmo Kang | Rokyeon Kim | Seungwu Han | Young‐Woo Son
Source: arXiv (Cornell University) (preprint, 2025-03-04)
Field: Materials Science — Materials Chemistry

Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational efficiency. We apply the new method to binary and ternary systems such as GeSe and Ge-Sb-Te (GST) compounds, well-known materials for non-volatile phase-change memory and related technologies. Using a training set of {\it ab initio} electronic energy bands and rotation-covariant descriptors for various GeSe and GST compounds, we generate transferable EPs for Ge, Se, Sb, and Te. We demonstrate that the new ML model accurately reproduces the energy bands and wavefunctions of structures outside the training set, closely matching first-principles calculations. This accuracy is achieved with significantly lower computational costs due to the elimination of self-consistency iterations and the reduced size of the plane-wave basis set. Notably, the method maintains accuracy even for diverse local atomic environments, such as amorphous phases or larger systems not explicitly included in the training set. Pseudopotential ; Ternary operation ; Transferability ; Matching (statistics) ; Binary number ; Energy (signal processing) ; Amorphous solid ; Wave function Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science