jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S Adhithya | J. Kathirvelan
Source: Results in Engineering (article, 2025-03-30)
Field: Chemistry — Analytical Chemistry

Computer vision-based detection and classification of chemically ripened bananas and papayas at vendor site through deep learning AI models using real-time dataset • Chemically ripened bananas and papaya pose significant health risks due to carcinogens and harmful substances. • Existing laboratory-based techniques and electrochemical gas sensors are cumbersome and face challenges in accurately discriminating between naturally and chemically ripened bananas and papaya on site. • The study develops an onsite device using advanced computer vision (CV) and deep learning (DL) techniques to detect chemically ripened bananas and papaya efficiently. • Among various models, CNN with Orthogonal Softmax Layers (CNN with OSL) achieved the accuracy of 91.58 % in discrimination of bananas and highest accuracy of 96.32 % in discrimination of papaya. It was implemented in real-time using Raspberry Pi, demonstrating its practical feasibility. Chemically ripened bananas and papayas, which pose health risks due to carcinogens and other harmful substances, are increasingly common worldwide. Traditional laboratory-based techniques for distinguishing naturally ripened from chemically ripened bananas and papayas face challenges due to their cumbersome processes. This study aims to address these limitations by developing an onsite device to detect chemically ripened bananas, offering a faster and more cost-effective solution. The research employs advanced computer vision (CV) and deep learning (DL) techniques, including support vector machines (SVMs), logistic regressions, recurrent neural networks (RNNs), artificial neural networks (ANNs), convolutional neural networks (CNNs), CNNs with orthogonal softmax layers (CNNs with OSLs), and regional CNNs. A custom real-time dataset of naturally and chemically ripened bananas from five different species and papaya was created for this purpose. The models were trained and tested on this dataset, with the CNN with OSL achieving the highest accuracy of 91.58 % and 96.32 % in a computational time of 2.24 s of discriminating banana and papaya. To scale up this approach, the system was implemented in real time using a Raspberry Pi board and a Pi camera, capturing live images of the fruits and processing them via trained models to detect chemically ripened fruits efficiently and accurately. This work demonstrates the potential of CV and DL techniques to combat fruit adulteration, providing a reliable and automated solution for ensuring food safety. The findings suggest that a CNN with OSL can accurately detect adulterated fruits, making it a promising tool for future developments in this field. Vendor ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Business ; Marketing Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Ganesh Kumar Nayak | Prashanth Srinivasan | Juraj Todt | R. Daniel | Paolo Nicolini | David Holec
Source: Computational Materials Science (article, 2025-01-06)
Field: Materials Science — Materials Chemistry

Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: A case study of silicon nitride Ab initio calculations represent the technique of election to study material system, however, they present severe limitations in terms of the size of the system that can be simulated. Often, the results in the simulation of amorphous materials depend dramatically on the size of the system. Here, we overcome this limitation for the specific case of mechanical properties of amorphous silicon nitride (a-Si 3 N 4 ) by training a machine learning (ML) interatomic model. Our strategy is based on the generation of targeted training sets, which also include deliberately stressed structures. Using this dataset, we trained a moment tensor potential (MTP) for a-Si 3 N 4 . We show that molecular dynamics simulations using the ML model on much larger systems yield elastically isotropic response and can reproduce experimental measurement. To do so, models containing at least ≈ 3 , 500 atoms are necessary. The Young’s modulus calculated from the MTP at room temperature is 220 GPa , which is very well in agreement with the nanoindentation measurement. Our study demonstrates the broader impact of machine learning potentials for predicting structural and mechanical properties, even for complex amorphous structures. Silicon nitride ; Amorphous solid ; Materials science ; Nitride ; Silicon ; Nanotechnology ; Amorphous silicon ; Engineering physics ; Optoelectronics ; Engineering ; Chemistry ; Crystalline silicon ; Crystallography Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Thermal properties of materials
Authors: Raouf Hassan | Alireza Baghban
Source: Case Studies in Thermal Engineering (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework This study presents a comprehensive data-driven framework to accurately estimate the thermal conductivity of nano-enhanced phase change materials (NEPCMs) using machine learning. A dataset of 482 samples, incorporating various nanoparticle types, concentrations, PCM types, and operating temperatures, was curated and refined using the Monte Carlo Outlier Detection algorithm. Extensive machine learning algorithms were explored; however, CatBoost, XGBoost, ANN, Random Forest, and Gradient Boosting emerged as the most accurate models. Among them, CatBoost achieved the highest predictive performance with an R 2 of 0.979 and the lowest mean squared error (MSE) of 0.006 on the test set. SHAP analysis revealed that nanoparticle concentration was the most influential feature. These findings highlight the effectiveness of the proposed approach in capturing the complex interactions governing thermal conductivity in NEPCMs and provide valuable insights for material design and optimization. Thermal conductivity ; Phase change ; Computer science ; Phase (matter) ; Carbon fibers ; Materials science ; Thermal ; Thermodynamics ; Composite material ; Algorithm ; Chemistry ; Physics Thermal properties of materials ; Phase Change Materials Research ; Machine Learning in Materials Science
Authors: Jingzhuo Zhou | Zhengyu Gu | Xiaobing Wang | Xiuyun Zhang | Yixin Ouyang | Li Shi
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Screening of single-atom catalysts for CO<sub>2</sub> electroreduction to CH<sub>4</sub> using DFT calculations and machine learning An enhanced high-throughput DFT screening model additionally incorporates *OH reduction to refine selectivity, identifying 9 CO 2 RR-active TM@C 5 N SACs. Machine learning reveals key descriptors, guiding catalyst design. Catalysis ; Renewable energy ; Methane ; Atom (system on chip) ; Reduction (mathematics) ; Materials science ; Redox ; Combinatorial chemistry ; Nanotechnology ; Chemistry ; Photochemistry ; Computer science ; Inorganic chemistry ; Engineering ; Organic chemistry ; Embedded system ; Electrical engineering Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Jiawei Chen | Yuming Gu | Qin Zhu | Yating Gu | Xinyi Liang | Jing Ma
Source: Langmuir (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N<sub>2</sub> and CO<sub>2</sub> Reduction Reactions The applications of machine learning (ML) in complex interfacial interactions are hindered by the time-consuming process of manual feature selection and model construction. An automated ML program was implemented with four subsequent steps: data distribution analysis, dimensionality reduction and clustering, feature selection, and model optimization. Without the need of manual intervention, the descriptors of metal charge variance (Δ<i>Q</i><sub>CT</sub>) and electronegativity of substrate (χ<sub>sub</sub>) and metal (δχ<sub>M</sub>) were raised up with good performance in predicting electrochemical reaction energies for both nitrogen reduction reaction (NRR) and CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR) on metal-zeolites and MoS<sub>2</sub> surfaces. The important role of interfacial interactions in tuning the catalytic reactivity in NRR and CO<sub>2</sub>RR was highlighted from SHAP analysis. It was proposed that Fe-, Cr-, Zn-, Nb-, and Ta-zeolites are favorable catalysts for NRR, while Ni-zeolite showed a preference for CO<sub>2</sub>RR. An elongated bond of N<sub>2</sub> or a bent configuration of CO<sub>2</sub> was shown in V-, Co-, and Mo-zeolites, indicating that the molecule could be activated after the adsorption in both NRR and CO<sub>2</sub>RR pathways. The generalizability of the automatically built ML model is demonstrated from applications to other catalytic systems such as metal-organic frameworks and SiO<sub>2</sub> surfaces. The automated ML program is a useful tool to accelerate the data-driven exploration of relationship between structures and material properties without the need of manual feature selection. Catalysis ; Chemistry ; Electronegativity ; Redox ; Dimensionality reduction ; Transition metal ; Reactivity (psychology) ; Metal ; Computer science ; Inorganic chemistry ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Nitasha Habib | Hasan Can Gülbalkan | Ahmet Safa Aydogdu | Alper Uzun | Seda Keskın
Source: Coordination Chemistry Reviews (article, 2025-04-25)
Field: Chemistry — Inorganic Chemistry

Toward rational design of ionic liquid/Metal-Organic Framework composites for efficient gas separations: Combining molecular modeling, machine learning, and experiments to move beyond trial-and-error This review presents a comprehensive overview of the emerging field of ionic liquid/metal-organic framework (IL/MOF) composites, which synergistically combine the unique properties of two material families, ILs and MOFs, for adsorption- and membrane-based gas separation applications. ILs, with their low volatility and tunable chemical properties, enhance the functionality of MOFs, which are highly porous materials characterized by their large surface areas and adjustable pore sizes. The effective integration of these two materials through various synthesis techniques has led to the development of novel IL/MOF composites exhibiting superior gas adsorption and separation capabilities, particularly for CO 2 capture, compared to pristine MOFs. The use of these composites as fillers in polymers resulted in mixed matrix membranes with enhanced selectivities. This field is advancing rapidly, yet the design of IL/MOF composites still relies on a trial-and-error approach. After providing an overview of the current state-of-the-art in gas separation applications of IL/MOF composites, we specifically focused on the pivotal role of computational studies in both complementing and, beyond that, guiding the experimental design of novel IL/MOF composites. The importance of harnessing experiments, computational modeling, and machine learning methods to accelerate the discovery of new IL/MOF composites as adsorbents and membranes was discussed with recent examples. Finally, we addressed the current challenges and future opportunities in this rapidly evolving field, emphasizing the significant potential of IL/MOF composites to revolutionize the current gas adsorption and separation technologies. • Ionic liquid/metal-organic framework (IL/MOF) composites were reviewed. • Gas adsorption and separation applications of IL/MOF composites were addressed. • Combining experiments and modeling to design novel composites was discussed. Chemistry ; Ionic liquid ; Rational design ; Metal ; Nanotechnology ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Ionic liquids properties and applications
Authors: Tuğba Hacıefendioğlu | Erol Yıldırım
Source: Journal of Chemical Information and Modeling (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Band Gap and Reorganization Energy Prediction of Conducting Polymers by the Integration of Machine Learning and Density Functional Theory The performance and reliability of machine learning (ML)-quantitative structure-property relationship (QSPR) models depend on the quality, size, and diversity of the data set used for model training. In this study, we manually curated a large-scale data set containing 3120 donor-acceptor (D-A) conjugated polymers (CPs) by selecting the most utilized 60 donors and 52 acceptors. This data set serves as a valuable resource for ML-based prediction of key electronic properties such as band gap energy (<i>E</i><sub>g</sub>) and hole reorganization energy (λ<sub>h</sub>), calculated using density functional theory (DFT) to advance organic photovoltaics (OPV). Beyond data set construction, we systematically investigated how different descriptor and fingerprint types impact performance of the ML model. Recognizing that not all features contributed equally to the model performance, we conducted an in-depth analysis to identify the most informative descriptors for the fundamental optoelectronic properties. Our findings show that kernel partial least-squares (KPLS) regression utilizing radial and molprint2D fingerprints achieved the highest accuracy in predicting <i>E</i><sub>g</sub>, with <i>R</i><sup>2</sup> values of 0.899 and 0.897, respectively. For λ<sub>h</sub> prediction, models integrating electronic descriptors such as frontier orbital energy levels significantly improved performance, achieving an <i>R</i><sup>2</sup> value of 0.830. This study provides a comprehensive investigation of how different descriptors influence model performance in OPV research. By analyzing why certain models succeed while others fail, our findings offer insight into feature selection and data set optimization for accurate target property prediction in organic electronics. The developed ML models provide a predictive framework for high-performance OPV materials design, significantly reducing the reliance on labor-intensive experimental procedures and computationally expensive first-principle calculations. Density functional theory ; Polymer ; Energy (signal processing) ; Artificial intelligence ; Computer science ; Materials science ; Machine learning ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Jetsada Posom | Chirawan Saenphon | Sirirak Ditcharoen | Lakkana Pitak | Panmanas Sirisomboon | Kanvisit Maraphum
Source: Journal of Food Composition and Analysis (article, 2025-02-24)
Field: Chemistry — Analytical Chemistry

Deep neural networks (DNNs) chemical compositions estimation of fresh durian in-line via near infrared spectroscopy Near-infrared spectroscopy ; Spectroscopy ; Line (geometry) ; Infrared spectroscopy ; Infrared ; Chemistry ; Environmental science ; Organic chemistry ; Physics ; Mathematics ; Optics ; Astronomy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Zirak Khan | Seung-Chul Yoon | Suchendra M. Bhandarkar
Source: Sensors (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

Deep Learning Model Compression and Hardware Acceleration for High-Performance Foreign Material Detection on Poultry Meat Using NIR Hyperspectral Imaging Ensuring the safety and quality of poultry products requires efficient detection and removal of foreign materials during processing. Hyperspectral imaging (HSI) offers a non-invasive mechanism to capture detailed spatial and spectral information, enabling the discrimination of different types of contaminants from poultry muscle and non-muscle external tissues. When integrated with advanced deep learning (DL) models, HSI systems can achieve high accuracy in detecting foreign materials. However, the high dimensionality of HSI data, the computational complexity of DL models, and the high-paced nature of poultry processing environments pose challenges for real-time implementation in industrial settings, where the speed of imaging and decision-making is critical. In this study, we address these challenges by optimizing DL inference for HSI-based foreign material detection through a combination of post-training quantization and hardware acceleration techniques. We leveraged hardware acceleration utilizing the TensorRT module for NVIDIA GPU to enhance inference speed. Additionally, we applied half-precision (called FP16) post-training quantization to reduce the precision of model parameters, decreasing memory usage and computational requirements without any loss in model accuracy. We conducted simulations using two hypothetical hyperspectral line-scan cameras to evaluate the feasibility of real-time detection in industrial conditions. The simulation results demonstrated that our optimized models could achieve inference times compatible with the line speeds of poultry processing lines between 140 and 250 birds per minute, indicating the potential for real-time deployment. Specifically, the proposed inference method, optimized through hardware acceleration and model compression, achieved reductions in inference time of up to five times compared to unoptimized, traditional GPU-based inference. In addition, it resulted in a 50% decrease in model size while maintaining high detection accuracy that was also comparable to the original model. Our findings suggest that the integration of post-training quantization and hardware acceleration is an effective strategy for overcoming the computational bottlenecks associated with DL inference on HSI data. Hyperspectral imaging ; Computer science ; Inference ; Acceleration ; Quantization (signal processing) ; Artificial intelligence ; Speedup ; Deep learning ; Real-time computing ; Computer engineering ; Machine learning ; Simulation ; Computer vision ; Parallel computing Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Zhen Tan | Xinyu Li | Rui Bai | Chang Guo | Xiao Han | Qinfeng Shi | Jian Zhang | Haobo Li
Source: ACS Catalysis (article, 2025-07-16)
Field: Materials Science — Materials Chemistry

AI for Complex Catalytic Systems: High-Entropy Alloys in Electrocatalytic Acetylene Semihydrogenation AI-driven big data set analysis offers opportunities for theoretical research on systems that combine complex catalyst materials with intricate catalytic reactions. In this study, we explore high-entropy alloys (HEAs) as potential electrocatalysts for the electrochemical semihydrogenation of acetylene. HEAs provide a variety of surface active sites due to the diverse combination of constituent elements, while the presence of bidentate dicarbon species further complicates surface interactions. By integrating density functional theory computations, geometric optimizer development, and graph neural network-based machine learning predictions, we efficiently compile a comprehensive database of 52,900 adsorption properties for AgAuCuNiPd HEA surfaces. Lasso regression and t-SNE projection reveal the distinct influences of the five metal components on adsorption and reaction properties. Using Cu as a reference, logistic regression assesses the potential for other components to surpass Cu in terms of catalytic activity and selectivity toward ethylene. Our findings suggest that, while HEAs can enhance the reaction, the ternary AgAuCu alloy achieves optimal results, indicating that high entropy is not essential. This research methodology can be extended to other complex catalytic systems, providing valuable insights into catalytic mechanisms and facilitating experimental endeavors. Catalysis ; Acetylene ; Materials science ; Chemistry ; Combinatorial chemistry ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Fatima Živić | Ana Kaplarevic Malisic | Nenad Grujović | Boban Stojanović | Miloš Ivanović
Source: Materials Today Communications (review, 2025-08-07)
Field: Materials Science — Materials Chemistry

Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials Materials science ; Materials informatics ; Informatics ; Nanotechnology ; Porous medium ; Porosity ; Health informatics ; Composite material ; Engineering ; Nursing Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ting Ying | Rui Zhu | Yao Wang | Yongfa Zhu
Source: Advanced Functional Materials (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Guided Single Atom Catalysts: From Precise Design to Industrial‐Grade Preparation Abstract Single‐atom catalysts (SACs) have transformed heterogeneous catalysis via atomic‐level active site control, but bridging atomic‐scale design with structurally consistent, scalable manufacturing remains challenging. This review documents artificial intelligence (AI)‐driven advances in SACs research, covering rational design, dynamic characterization, and scalable synthesis. It highlights integrating physics‐based simulations with machine learning (ML) models. ML leverages coordination‐sensitive descriptors to predict catalytic performance and guide inverse design. Emerging techniques such as graph neural network (GNN) enhanced operando X‐ray absorption spectroscopy (XAS) are featured, decoding real‐time atomic coordination dynamics. A closed‐loop “design‐validate‐manufacture” framework is discussed, integrating digital twins with autonomous robotic synthesis to ensure structural fidelity from models to industrial production and minimize coordination distortion during scaling. Addressing challenges like small data learning and industrial structural inconsistencies, the review proposes a unified framework for atomic precision manufacturing of SACs, outlining an intelligent roadmap for next‐generation energy and environmental applications. Materials science ; Catalysis ; Atom (system on chip) ; Nanotechnology ; Chemical engineering ; Computer science ; Organic chemistry ; Engineering ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions