jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhuoxuan Zhu | Yida Dong | Mingxi Chen | Zihan Geng | Mei Shen
Source: Journal of Physics Conference Series (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Optical Phase-Change Materials for Integrated Photonics through Data-Driven Approaches Optical phase-change materials (OPCMs) enable next-generation reconfigurable and non-volatile photonic devices through reversible transitions between amorphous and crystalline states with distinct optical properties. Their large refractive index contrast (Δn) and tunable characteristics support applications in optical memory, tunable metasurfaces, and photonic neural networks. However, conventional OPCMs such as Ge 2 Sb 2 Te 5 suffer from high crystalline-state extinction coefficients (k c ), limiting large-scale integration. Moreover, trial-and-error material discovery is time-consuming and costly. Here, we establish a high-throughput database of 2308 compositions containing S, Sc, Ge, Se, In, Sb, Te, and Bi, with measured Δn and k c at 1550 nm. Machine learning models trained on elemental and stoichiometric features achieve R 2 &gt; 0.96 for both properties. SHAP analysis identifies atomic properties, electronic structure, atomic size, and thermodynamic parameters as key features, and reveals that S, Se, and Sc reduce both Δn and k c . This framework enables accurate property prediction and provides design guidelines for low-loss OPCMs, offering an effective pathway for the rapid development of advanced integrated photonic materials. Photonics ; Limiting ; Amorphous solid ; Refractive index ; High-refractive-index polymer ; Key (lock) ; 3D optical data storage ; Stoichiometry Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Chemical and Physical Properties of Materials
Authors: Ashutosh A. Paraye | Debashis Kundu
Source: International Journal of Hydrogen Energy (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Artificial intelligence driven framework of hydrogen yield from reactions of alanates Artificial neural network ; Hydrogen storage ; Hydrogen ; Mean squared error ; Normalization (sociology) ; Hydrogen production Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: R S Thejaswini
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

Detection of Adulteration in Fruits and Vegetables using Deep Learning Food adulteration and poor produce quality remain pressing global concerns, directly impacting consumer health and food safety. Fruits and vegetables are often treated with artificial coatings, dyes, and surface chemicals to enhance their appearance and shelf life. Such adulteration practices, while visually appealing, can introduce harmful substances into the food chain. To address this issue, the proposed study presents an intelligent, automated system for quality and safety assessment of fruits and vegetables using deep learning and advanced image processing techniques. The system leverages two publicly available datasets—Fruits Detection and Quality Analysis, and Vegetable Quality Detection—comprising authentic and adulterated samples. Image preprocessing steps such as binarization, adaptive thresholding, grayscale conversion, and watershed segmentation are applied to enhance contrast, isolate regions of interest, and improve feature extraction. The processed images are then analyzed using a VGG16-based Convolutional Neural Network (CNN) model fine-tuned through transfer learning to detect adulteration and evaluate surface quality. Based on the classification results, the system further performs a safety assessment, categorizing produce as safe or unsafe for consumption. This dual-layered approach ensures both visual quality grading and health-oriented safety prediction. Experimental results demonstrate that the model achieves high accuracy and robust generalization across varied lighting and texture conditions. The proposed system provides a rapid, non-invasive, and reliable solution for real-time food quality monitoring, aiding consumers, vendors, and regulatory authorities in ensuring safer and more transparent food distribution Deep learning ; Convolutional neural network ; SAFER ; Food safety ; Preprocessor ; Transfer of learning ; Grayscale ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Listeria monocytogenes in Food Safety
Authors: Yueyue Yuan | Chong Chen | Qingqing Huang | Ming Gong | Zi Ye
Source: Journal of Photochemistry and Photobiology A Chemistry (article, 2025-07-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The determination of SERS activity of Au@Ag polyhedral nanostructures and the detection of hair loss types based on transformer Chemistry ; Nanostructure ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Hao Guo (346528) | Chunlei Dong (323987) | Qingquan Han (11048009) | Hongyu Jiang (8537316) | Hongji Sun (18290823) | Xuemei Sun (811050) | Songlin Zhang (1580836) | Huisheng Peng (1368006)
Source: Figshare (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Advancing CarbonNanotube Fibers: Addressing Challengesfrom Production to Application Carbon nanotube (CNT) fibers, renowned for their theoretically high tensile strength, low density, and outstanding electrical conductivity, are promising candidates for cutting-edge applications in wearable electronics, bioengineering, and aerospace engineering. Despite their immense potential, the widespread adoption of CNT fibers faces critical barriers, including the challenge of enhancing macroscopic fiber performance and achieving scalable, consistent production. The extraordinary intrinsic properties of individual CNTs are not fully transferred to macroscopic fibers due to weak intertube interactions, misalignment, and structural defects. Among the available production methods, floating catalyst chemical vapor deposition stands out for its promise to enable the large-scale synthesis of CNT fibers. However, achieving consistent quality and scalability via this technique remains a significant obstacle. This outlook highlights the importance of innovative strategies for multiscale performance optimization and advances in scalable fabrication methodologies. A distinctive perspective on CNT fiber production is provided, emphasizing the integration of machine learning with process optimization strategies to enhance the uniformity and efficiency. The outlook systematically discusses these challenges, exploring strategies for multiscale performance optimization, scaled-up fabrication methodologies, and efficient manufacturing processes. Additionally, it examines the transformative applications of CNT fibers across diverse industries, underscoring their potential to revolutionize next-generation technologies. Carbon nanotube ; Fabrication ; Aerospace ; Scalability ; Production (economics) ; Fiber ; Chemical vapor deposition ; Process (computing) Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Electrospun Nanofibers in Biomedical Applications
Authors: Benedict Saunders | Lukas Hörmann | Reinhard J. Maurer
Source: ArXiv.org (preprint, 2025-09-10)
Field: Materials Science — Materials Chemistry

Comprehensive Structure Exploration and Thermodynamics of Heteroatom Doped Graphene Superstructures Graphene has been studied in detail due to its mechanical, electrical, and thermal properties. It is well documented that the introduction of dopants or defects in the lattice can be used to tune material properties for a specific application, such as in electronics, sensors, or catalysis. To design graphene with specific properties, one must achieve control over the composition and concentration of defects. This requires a fundamental understanding of the stability of defects and their interaction in a superstructure. We present a comprehensive defect structure determination approach that enables close to exhaustive enumeration of all relevant defect structures. The approach uses a combination of Density Functional Theory and machine learning to build a transferable energy model for defect formation. Henceforth, we show the capabilities of our approach for a proof-of-principle application on free-standing graphene with heteroatom defects. This allows us to provide physical insights into defect interactions and to establish a thermodynamic model to investigate how temperature affects the configuration space of doped graphene. Graphene ; Heteroatom ; Dopant ; Density functional theory ; Doping ; Graphene nanoribbons ; Thermal stability ; Lattice (music) Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: A.R. Hosseinzadeh | Mansooreh Soleimani | Maryam Takht Ravanchi
Source: Process Safety and Environmental Protection (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Cost-optimized design of front-end acetylene hydrogenation using artificial neural networks Given that acetylene is a poison to ethylene polymerization catalysts, this research studied the design for a cost-effective optimization of the front-end acetylene hydrogenation unit. The design capacity is 0.6 million ton/annum (for a commercial petrochemical plant) to reduce the acetylene concentration to five ppm while maintaining the unit’s controllability. The study started by modeling an adiabatic fixed-bed pseudo-homogeneous reactor with Pd-Ag/Al 2 O 3 catalyst. The hydrogenation plant was designed using the validated model and literature. It was also possible to do sensitivity analysis on the important operational parameters, like temperature, pressure, the number of catalytic beds, and volumes, in order to get 12467 samples ready for training the artificial neural network (ANN) model. In the next step, the economic potential of the artificial neural network model was optimized with the genetic algorithm (GA). In this optimization problem, economic potential was the fitness function, and utility consumption in heat exchangers, feed pressure, bed volumes, and presence of the third bed were the optimization variables. Optimization using GA-ANN model, GA for numerical model, and Bayesian for ANN revealed that the GA-ANN approach significantly reduced function evaluations (27,900 vs. 51,900 in GA-Numerical) and achieved the highest economic potential (1.997 million USD annually). The optimization led to a reduction in normal operating temperature and inlet pressure. • Design of a reliable, economically optimized front-end acetylene hydrogenation unit for a commercial-scale petrochemical olefin production plant. • The implementation of an ANN-based optimization approach to enhance economic potential. • Comparative analyses of GA-ANN, GA-Numerical, and Bayesian Optimization methods. Acetylene ; Artificial neural network ; Front and back ends ; Front (military) ; Computer science ; Engineering ; Environmental science ; Materials science ; Artificial intelligence ; Chemistry ; Mechanical engineering ; Organic chemistry Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysis and Oxidation Reactions
Authors: Ke Wang | Wei Cao | Kunchi Xie | Shuyun Gu | Siyao Li | Zhiwen Qi | Zhen Song | Zhi Xu
Source: AIChE Journal (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Rationally engineering interfacial polymerization toward covalent organic framework membranes mediated by ionic liquids Abstract Liquid–liquid interfacial polymerization (IP) serves as a facile method for fabricating covalent organic framework (COF) membranes, while designing task‐specific IP systems remains a huge challenge. This work proposes a rational strategy to achieve controlled IP by monomer–catalyst–biphasic solvents matching, integrating thermodynamic predictions and dynamic insights. For the IP engineering, ionic liquids (ILs) are introduced into the biphasic solvent system due to their unique physicochemical properties. Utilizing conductor‐like screening model for realistic solvents (COSMO‐RS) calculations, deep learning‐aided physical properties predictions, and molecular dynamics simulations, 10 promising pairs were identified from 622 candidates. This strategy enables the transition from highly cross‐linked amorphous membranes to uniform crystalline membranes with reduced thickness (from 520 to 124 nm), synergizing thermodynamic partition and diffusion regulation. The membranes exhibit increased water permeance (from 0.022 to 7.43 L·m −2 ·h −1 ·bar −1 ) and high antibiotic desalination efficiency. Furthermore, this strategy is successfully extended to other COF membranes, enriching the tuning flexibility of IP system for the development of novel COF membranes. Membrane ; Ionic liquid ; Permeance ; Polymerization ; Amorphous solid ; Interfacial polymerization ; Covalent organic framework ; Molecular dynamics Covalent Organic Framework Applications ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Zong-Rong Ye (12447414) | Yu-Huan Huang (21573401) | Berlin Chen (12447420) | Ming-Kang Tsai (1798330)
Source: Figshare (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

ModelingSolvatochromic Effect by a Two-Step MachineLearning Approach for Organic Chromophores Solvatochromism, also known as the environment-induced modulation of photophysical properties, plays a critical role in the design of fluorescent probes, sensing systems, and optical materials. Traditional machine learning models often struggle to disentangle solute-intrinsic effects from solvent-induced shifts, limiting both interpretability and predictive robustness. To address this challenge, we propose a two-step regression framework that models fluorescence-related properties by first estimating a solute-only baseline, followed by a correction step capturing solvent contributions. Utilizing Morgan fingerprints for molecular representation and random forest regression for learning, the model was benchmarked on five comprehensive data sets encompassing wavelength of maximum adsorption and wavelength of maximum emission. Across all data sets, the two-step approach consistently outperforms conventional one-pot models in both accuracy and mean absolute error. For instance, the <i>Q</i><sup>2</sup> and MAE of the testing set for predicting λ<sub>emi</sub> of the Deep4Chem data set are 0.844 and 27.8 nm, respectively, using a two-step approach, outperforming the corresponding results at 0.767 and 33.1 nm, respectively, using a one-pot model. Permutation importance analysis reveals a chemically intuitive rebalancing of feature influence: solute descriptors dominate prediction, while solvent effects are modeled as secondary perturbations. This modular design not only enhances predictive power but also aligns more closely with physical insights. The framework offers an alternative direction for chemically interpretable, data-driven modeling of the solvatochromic behavior in molecular systems. Interpretability ; Solvatochromism ; Representation (politics) ; Random forest ; Chromophore ; Feature (linguistics) ; Molecular descriptor ; Data set ; Set (abstract data type) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Sensors and Ion Detection
Authors: Ruoxia Chen | Kai Yang | Morten M. Smedskjær | N. M. Anoop Krishnan | Jaime Marian | Fabian Rosner
Source: ArXiv.org (preprint, 2025-10-15)
Field: Materials Science — Ceramics and Composites

Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties The accuracy of molecular simulations is fundamentally limited by the interatomic potentials that govern atomic interactions. Traditional potential development, which relies heavily on ab initio calculations, frequently struggles to reproduce the experimentally observed properties that govern real material behavior. To address this challenge, we present a machine learning-driven, active-learning optimization framework for optimizing classical interatomic potentials to reproduce experimental properties. Our method, here showcased on soda-lime borosilicate glasses, targets both global (density) and local (boron coordination) structural features across a wide range of compositions. By combining a surrogate model with iterative active learning, the framework efficiently explores a five-dimensional parameter space using only 400 molecular dynamics simulations over 17 iterations, making it highly data-efficient and eliminating the need for extensive simulation campaigns. Two transferable parameter sets are identified, each demonstrating good agreement with experimental measurements, including glass density, fraction of four-fold boron, and X-ray structure factor. The framework effectively captures and manages inherent trade-offs between structural objectives and compositional regimes, providing insights into the coordination behavior of boron in complex glass networks. The resulting classical force fields are generalizable and do not require reparameterization for individual compositions. Altogether, this work offers a scalable and experimentally grounded approach for developing transferable interatomic potentials suitable for a broad range of materials, including multi-component glass systems, and beyond. Interatomic potential ; Molecular dynamics ; Borosilicate glass ; Work (physics) ; Range (aeronautics) ; Ab initio ; Space (punctuation) Glass properties and applications ; Aerogels and thermal insulation ; Additive Manufacturing and 3D Printing Technologies
Authors: Z. Evrim Kanat
Source: Black Sea Journal of Engineering and Science (article, 2025-11-12)
Field: Materials Science — Polymers and Plastics

Investigation and Prediction the Permeability of Woollen Fabrics The water vapor and air permeability properties of fabrics are the determining factors for thermal comfort. It is stated in the literature that the hygroscopic structure of wool fibre and the fibre surface properties improve the water vapor permeability property, thus increasing the sense of comfort. In this study water vapor and air permeability of 16 woollen fabric were investigated. It was shown that the effect of finishing process, weave type and raw material on air permeability is significant. In case of water vapor permeability, the effect of finishing process and weave type was found to be significant in the 95% confidence interval, while the effect of raw material was not found to be significant. Furthermore, permeability properties of woollen fabrics were predicted both multiple linear regression and artificial neural network. The R2 values of the models were 0.989 and 0.485 obtained with multiple linear regression and 0.988 and 0.773 obtained with artificial neural network, for air and water vapor permeability, respectively. Results of the artificial neural network were quite good especially for water vapor permeability. Water vapor ; Air permeability specific surface ; Permeability (electromagnetism) ; Raw material ; Wool ; Thermal Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Wood Treatment and Properties
Authors: Kexin Wang (758568) | Haishan Yu (3138672) | Xiangwei Lu (21820653) | Kai Chen (129938)
Source: Figshare (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Deconvolution of Mixed X‑rayAbsorption Spectra X-ray absorption spectroscopy (XAS) plays a crucial role in determining the oxidation states, local atomic structures, and electronic configurations of materials. However, the interpretation of complex spectra with multiple ion sites and varying local chemical environments can be labor-intensive and requires specialized expertise, presenting challenges for researchers. To address these issues, this study employs machine learning (ML) techniques to analyze mixed XAS data, with a particular focus on the cobalt (Co) element. We constructed a simulated data set of L-edge spectra, incorporating various combinations and energy shifts produced from multiplet calculations. Through a systematic evaluation of dimensionality reduction and ML methodologies, we identified a combination that achieved both high accuracy and efficiency in classification tasks. Our approach involved isolating individual components from the mixed spectra and implementing an automatic fitting algorithm to extract critical parameters, such as component proportions and Gaussian broadening. The fitting results closely aligned with the input spectra, thereby validating our methodology. This innovative process facilitates the determination of valence states, local crystal field parameters, and composition ratios, significantly enhancing the efficiency of spectral analysis. Deconvolution ; Spectral line ; Curse of dimensionality ; Dimensionality reduction ; Multiplet ; Gaussian ; Absorption spectroscopy ; Principal component analysis ; Valence (chemistry) ; Gaussian process Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Zong-Rong Ye | Yu–Huan Huang | Berlin Chen | Ming‐Kang Tsai
Source: ACS Engineering Au (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Modeling Solvatochromic Effect by a Two-Step Machine Learning Approach for Organic Chromophores Solvatochromism, also known as the environment-induced modulation of photophysical properties, plays a critical role in the design of fluorescent probes, sensing systems, and optical materials. Traditional machine learning models often struggle to disentangle solute-intrinsic effects from solvent-induced shifts, limiting both interpretability and predictive robustness. To address this challenge, we propose a two-step regression framework that models fluorescence-related properties by first estimating a solute-only baseline, followed by a correction step capturing solvent contributions. Utilizing Morgan fingerprints for molecular representation and random forest regression for learning, the model was benchmarked on five comprehensive data sets encompassing wavelength of maximum adsorption and wavelength of maximum emission. Across all data sets, the two-step approach consistently outperforms conventional one-pot models in both accuracy and mean absolute error. For instance, the Q2 and MAE of the testing set for predicting λemi of the Deep4Chem data set are 0.844 and 27.8 nm, respectively, using a two-step approach, outperforming the corresponding results at 0.767 and 33.1 nm, respectively, using a one-pot model. Permutation importance analysis reveals a chemically intuitive rebalancing of feature influence: solute descriptors dominate prediction, while solvent effects are modeled as secondary perturbations. This modular design not only enhances predictive power but also aligns more closely with physical insights. The framework offers an alternative direction for chemically interpretable, data-driven modeling of the solvatochromic behavior in molecular systems. Interpretability ; Solvatochromism ; Representation (politics) ; Random forest ; Chromophore ; Feature (linguistics) ; Molecular descriptor ; Data set ; Set (abstract data type) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses