jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Tang | Caicai Xu | Zhangqi Zuo | Lei Chen | Muyuan Liu | Xufeng Guo | Simai Wang | Shitao Huang | Xi Xiao
Source: Water Research (article, 2025-10-10)
Field: Materials Science — Biomaterials

Bridging cheminformatics and water emergency response: An interpretable machine learning approach accelerates natural algaecide discovery Algal bloom ; Cheminformatics ; Workflow ; Bridging (networking) ; Scalability ; Aquatic ecosystem Diatoms and Algae Research ; Chemical synthesis and alkaloids ; Cephalopods and Marine Biology
Authors: Ahmed M. Fahmy
Source: Premier journal of science. (article, 2025-01-31)
Field: Materials Science — Biomaterials

Advancing Nanotechnology in Targeted Cancer Therapy: From Passive Targeting to Smart Delivery Systems With the help of nanotechnology, cancer treatment can be optimized while still keeping the level of side effects low. This includes recent advances that treat cancer on a molecular level, including mechanisms for targeting the delivery of therapeutic substances and, last, new intelligent delivery systems. Active targeting approaches by surface modification have increased tumor cell ingestion and decreased the required dose for treatment. Yet, enhanced permeability and retention, which is its capital effect, is functional as well, but at a very low impact, as studies show only 0.7% of the injected nanoparticles accumulate in the tumors. FDA-approved nanoformulations using liposomes and polymers exhibit significant clinical benefits owing to their improved pharmacokinetics and less toxicity. Significant obstacles impede market entry, including low production scalability, complex regulatory frameworks, and development costs exceeding $2.5 billion. The existence of biological impediments, notably the blood-brain barrier and intricate tumor microenvironments, exacerbates the challenges of drug administration. The advances in artificial intelligence and smart delivery systems have enhanced the design and efficacy of nanocarriers. Machine learning algorithms can predict the success rate in drug delivery with 92% accuracy when the process is continuously monitored, which enables dynamic treatment adjustment. Along with these, quantum dots and DNA nanostructures represent new technologies offering more targeted and personalized approaches to cancer treatment. However, there are some hurdles in need of overcoming, such as current regulation and manufacturing. Cancer therapy ; Nanotechnology ; Targeted therapy ; Cancer ; Medicine ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Weilu Tian | Lixuan Zang | Y. G. Li | Cheng Peng | Jinhua Zhang | Hebin Shao | Ruiqing Xian | Ru Sun | Fei Liu | Haining Tan | Peixue Ling
Source: Carbohydrate Polymers (article, 2025-08-23)
Field: Chemistry — Analytical Chemistry

A Transformer-based framework with generative spectral augmentation for online monitoring of hyaluronic acid fermentation Hyaluronic acid ; Generative grammar ; Transformer ; Fermentation ; Computer science ; Generative model ; Chemistry ; Process engineering ; Artificial intelligence ; Biochemistry ; Engineering ; Biology ; Electrical engineering Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Advanced Chemical Sensor Technologies
Authors: Fumiaki Ichihashi | Yoshio Takahashi | Toshiaki Tanigaki
Source: Microscopy (article, 2025-06-23)
Field: Materials Science — Surfaces, Coatings and Films

Reduction of membrane-derived noise using beam-tilt measurement and deep learning in observation using environmental cell Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell. Noise (video) ; Tilt (camera) ; Materials science ; Membrane ; Silicon nitride ; Transmission electron microscopy ; Cathode ray ; Electron microscope ; Optics ; Nanotechnology ; Silicon ; Optoelectronics ; Electron ; Artificial intelligence ; Computer science ; Image (mathematics) ; Chemistry ; Physics ; Engineering ; Mechanical engineering Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Geophysical and Geoelectrical Methods
Authors: Marcin Maździarz
Source: Journal of Computational Chemistry (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Suitability of Available Interatomic Potentials for Sn to Model Its 2D Allotropes The suitability of a range of interatomic potentials for elemental tin was evaluated in order to identify an appropriate potential for modeling the stanene (2D tin) allotropes. Structural and mechanical features of the flat (F), low-buckled (LB), high-buckled (HB), full dumbbell (FD), trigonal dumbbell (TD), honeycomb dumbbell (HD), and large honeycomb dumbbell (LHD) monolayer tin (stanene) phases, were gained by means of the density functional theory (DFT) and molecular statics (MS) calculations with ten different Tersoff, modified embedded atom method (MEAM), and machine-learning-based (ML-IAP) interatomic potentials. A systematic quantitative comparison and discussion of the results is reported. Interatomic potential ; Materials science ; Chemical physics ; Computational chemistry ; Molecular dynamics ; Statistical physics ; Chemistry ; Physics Machine Learning in Materials Science ; Thermal properties of materials ; Electronic Packaging and Soldering Technologies
Authors: Hilmi Saygın Sucuoğlu | Serra Aksoy | Pınar Demircioğlu | İsmail Böğrekçi
Source: Sustainability (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Sustainable Design and Lifecycle Prediction of Crusher Blades Through a Digital Replica-Based Predictive Prototyping Framework and Data-Efficient Machine Learning Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R2 = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Crusher ; Replica ; Rapid prototyping ; Computer science ; Virtual prototyping ; Engineering ; Manufacturing engineering ; Industrial engineering ; Systems engineering ; Engineering drawing ; Simulation ; Mechanical engineering Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced machining processes and optimization
Authors: Flaviano Della Pia | Giaan Kler-Young | Andrea Zen | Fabian Berger | Dario Alfè | Angelos Michaelides
Source: The Journal of Chemical Physics (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Interaction strength of carbon dioxide on graphene from periodic quantum diffusion Monte Carlo Despite the importance of graphene based carbon capture devices, an accurate estimate of the interaction strength of a carbon dioxide molecule with graphene from periodic calculations is lacking. In this work, we compute a fixed node quantum diffusion Monte Carlo reference value for the interaction energy of a carbon dioxide molecule with a periodic free-standing graphene sheet, obtaining a value of -152 ± 15 meV. In addition, we evaluate the performance of several widely used density functional theory approximations and foundation machine learning interatomic potentials, for both carbon dioxide and water adsorption on graphene, competitive processes that play an important role in carbon capture technologies. Among the approaches tested, the B86bPBE-XDM, PBE-D3, revPBE-D3, rev-vdW-DF2, SCAN+rVV10, and PBE0-D3-ATM functionals achieve the closest agreement with DMC for the carbon dioxide-graphene interaction. The vdW-DF2, rev-vdW-DF2, and PBE0-D4-ATM functionals perform better for the competitive adsorption of water and carbon dioxide. Graphene ; Quantum Monte Carlo ; Monte Carlo method ; Carbon dioxide ; Diffusion ; Statistical physics ; Diffusion Monte Carlo ; Materials science ; Quantum ; Physics ; Monte Carlo molecular modeling ; Nanotechnology ; Chemistry ; Quantum mechanics ; Mathematics ; Markov chain Monte Carlo ; Statistics Graphene research and applications ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yulun Wu | Parth Bansal | Yumeng Li
Source: Computational Materials Science (article, 2025-08-24)
Field: Materials Science — Materials Chemistry

Physics-informed multitask learning for microstructure-aware material metamodels of heterogeneous materials Constructing high-fidelity structure–property predictive models is crucial for investigating, developing and designing novel materials to enable technical innovations. Traditional approaches relying on physics-based simulations and experimental testing are often costly and time consuming, particularly for heterogeneous materials with complex microstructures. In this work, we present a multitask learning (MTL) framework to address two major challenges in data-driven modeling of such materials: (1) effective representation of microstructure features and (2) integration of multisource material properties with varying dimensionalities. Our approach leverages easily obtainable strain energy data to improve the prediction of complex displacement fields, thereby improving generalization and efficiency with limited training samples. This method can effectively fuse available limited data and significantly reduce the cost of generating a training dataset. In addition, the selection of input features have been investigated using different machine learning models on two types of heterogeneous material systems, the Material MNIST dataset and the two-phase fiber-reinforced composite material dataset, as the case study. The results of the case study show that the proposed model is effective and efficient in establishing predictive property models of heterogeneous material systems with complex microstructure. • Microstructure-aware material metamodels are developed to accurately predict the properties of complex heterogeneous materials through fusing multi-source data. • Multitask learning has been demonstrated to effectively fuse multi-source data, which significantly improve the accuracy, transferability and efficiency of the developed predictive models. • The selection of the microstructure encoder and property predictor is highly related to the characteristics of microstructure. Microstructure ; Materials science ; Computer science ; Nanotechnology ; Metallurgy Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced machining processes and optimization
Authors: Roel J. Leenhouts | Sebastien Jankelevitch | Roel Raike | S. M�ller | Florence H. Vermeire
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Chemistry — Organic Chemistry

Thermodynamics-informed Graph Neural Networks for Phase Transition Enthalpies Phase transition enthalpies, such as those for fusion, vaporization, and sublimation, are vital for understanding thermodynamic properties and aiding early-stage process design. However, measuring these properties is often time-consuming and costly, leading to increased interest in computational methods for fast and accurate predictions. Graph neural networks (GNNs), known for their ability to learn complex molecular representations, have emerged as state-of-the-art tools for predicting various thermophysical properties. Despite their success, GNNs do not inherently obey thermodynamic laws. In this study, we present a multitask GNN designed to predict vaporization, fusion, and sublimation enthalpies of organic compounds. We modified the loss function of the GNN, accounting for the thermodynamic cycle of the three phase transition enthalpies. To train the model, we digitized the extensive Chickos and Acree compendium, which encompasses 32,023 experimental measurements. Two approaches were explored: soft constraints, which guide the model toward thermodynamic consistency, and hard constraints, which enforce fully consistent predictions. The GNN achieved root mean squared errors (RMSEs) of 19.9 kJ/mol for sublimation, 11.0 kJ/mol for fusion, and 16.5 kJ/mol for vaporization enthalpies on the test set. Soft constraints were found to provide a good balance between accuracy and thermodynamic consistency, whereas hard constraints prioritized fidelity at the expense of predictive performance. When compared to the conventional Joback group contribution method the GNN demonstrated an improved accuracy and applicability range. This work underscores the potential of thermodynamics-informed GNNs for predicting thermodynamic properties accurately while maintaining consistency, paving the way for more reliable and efficient computational approaches. Thermodynamics ; Phase transition ; Computer science ; Artificial neural network ; Graph ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Physics Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Phase Equilibria and Thermodynamics
Authors: S. Murathathunyaluk | Maturada Jinorose | K. Janpetch | N. Chanthapanya | W. Sombatsri | A. Wongsricha | Rathachai Chawuthai | Sheila Mansouri | Amata Anantpinijwatna
Source: Measurement (article, 2025-02-08)
Field: Chemistry — Analytical Chemistry

Development of machine learning enhanced low-cost spectrophotometer for pesticide prediction Computer science ; Pesticide ; Machine learning ; Environmental science ; Artificial intelligence ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Mengmeng Li | Linna Guo | Yujie Wang
Source: Industrial Crops and Products (article, 2025-11-04)
Field: Chemistry — Analytical Chemistry

Enhanced prediction of total flavonoid in chrysanthemum using hyperspectral imaging and XGBoost-SHAP powered by WGAN data augmentation Chrysanthemum is one of the most cultivated herbal plants in China and is widely recognized for its potential health-promoting properties, many of which are attributed to its abundant flavonoid compounds. Traditionally, the determination of flavonoid content in chrysanthemum relies on labor-intensive wet chemical methods, while rapid, green, and non-destructive analytical alternatives remain a challenging. Herein, hyperspectral imaging combined with interpretable machine learning was employed for the quantitative prediction of total flavonoid. To address the limitations imposed by small sample sizes on model robustness, a Wasserstein generative adversarial network (WGAN) was introduced to simultaneously generate synthetic spectral and chemical data. Multidimensional evaluation metrics confirmed the effectiveness of WGAN in data augmentation, demonstrating superior data quality compared to conventional GAN and the deep convolutional GAN (DCGAN). The generated data closely resembling real data were obtained and validated through comprehensive qualitative and quantitative assessments. Leveraging the augmented dataset, an interpretable extreme gradient boosting - shapley additive explanations (XGBoost-SHAP) model achieved accurate prediction of total flavonoid content in chrysanthemum samples, yielding an R² of 0.8714 and a ratio of prediction to deviation (RPD) of 3.26, outperforming models trained solely on real data. The SHAP values further elucidated the contributions of characteristic wavelengths to model outputs, enhancing the transparency and interpretability of the predictive framework. This study thus presents a practical and scalable strategy for compositional prediction of agricultural products under data-constrained conditions, offering meaningful insights into the interpretability of machine learning-based spectroscopic modeling. • Hyperspectral imaging was used to predict total flavonoid in chrysanthemum. • WGAN was the optimal choice for data augmentation with 8000 training epochs. • XGBoost-SHAP model provides interpretable and accurate prediction. • The accuracies of all models were improved by the generated spectral data. Interpretability ; Hyperspectral imaging ; Pattern recognition (psychology) ; Vitexin ; Flavonoid ; Boosting (machine learning) ; Deep learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Sawyer VanZanten | Corin Wagen
Source: ChemRxiv (preprint, 2025-09-08)
Field: Materials Science — Materials Chemistry

Benchmarking OMol25-Trained Models onExperimental Reduction-Potential and Electron-Affinity Data The recent release of Meta's Open Molecules 2025 dataset (OMol25) has enabled the creation of pretrained NNPs that can predict the energy of unseen molecules in a variety of charge and spin states. However, these models do not explicitly consider charge- or spin-based physics, potentially impacting the accuracy of their predictions. In this work, we evaluate the ability of neural network potentials (NNPs) trained on OMol25 to predict experimental reduction-potential and electron-affinity values for a variety of main-group and organometallic species, and compare these NNPs to low-cost density-functional theory (DFT) and semiempirical-quantum-mechanical (SQM) methods. Surprisingly, we find that the tested OMol25-trained NNPs are as accurate or more accurate than low-cost DFT and SQM methods despite not considering explicit physics. Additionally, the tested OMol25-trained NNPs tend to predict the charge-related properties of organometallic species more accurately than the charge-related properties of main-group species, contrary to the trend for DFT and SQM methods. Variety (cybernetics) ; Benchmarking ; Artificial neural network ; Energy (signal processing) ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: M. Gerald Arul Selvan | Ananth Rajkumar | S. Prathap Singh | D. Elil Raja | Tushar Sonar
Source: Advances in Materials Science and Engineering (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Mechanical Characterization of Novel Fragrant Screwpine Fiber–Reinforced Polyester Composite and Development of a Mathematical Model With the Aid of Machine Learning The future markets of composite materials show a keen interest toward considering natural fibers due to the harmful effects of synthetic fibers and the environmental awareness of global manufacturers. The market demand and applications of natural fibers have increased momentously during the last decade and are expected to progress. The research study aims to develop a novel composite material encompassing indigenous fragrant screwpine fiber and polyester resin. For the purpose of determining the mechanical properties of the randomly oriented aromatic screwpine fiber–enhanced polyester composites, a range of fiber fractions (10, 20, 30, and 40 wt.%) and fiber lengths (3, 9 mm) was examined. The outcome points out that the tensile and flexural strength were superior for fiber loading up to 30 wt.% and then diminished, whereas the impact strength gradually increased. Based on the results obtained, mathematical equations were generated using machine learning software, and the predicted mechanical properties using the developed equations showed good conformity with the experimental values. Materials science ; Characterization (materials science) ; Composite number ; Composite material ; Polyester ; Fiber ; Polymer science ; Nanotechnology Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Mechanical Engineering and Vibrations Research
Authors: Bin Xie | Xinbing Zhao | Zheng-Dong Ma | Yujie Zhang | Jiasheng Dong | Yan Wang | Qiuhong Bai | Yehua Shen
Source: New Carbon Materials (article, 2025-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Modifying the pore structure of biomass-derived porous carbon for use in energy storage systems Materials science ; Porosity ; Carbon fibers ; Biomass (ecology) ; Porous medium ; Energy storage ; Chemical engineering ; Composite material ; Composite number ; Thermodynamics ; Power (physics) Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Hydrogen Storage and Materials
Authors: Saba Bashir | Issraa Beniani | Keisean Stevenson | Juan Manuel Montiel-León | Simon Forest | Malama Chisanga | Jean‐François Masson
Source: ACS Food Science & Technology (article, 2025-04-30)
Field: Chemistry — Organic Chemistry

Untargeted Profiling and Classification of Maple Syrup Quality from the Chromatographic and Mass Spectrometric Analysis of Volatile and Nonvolatile Chemical Fractions Maple syrup production faces challenges from the “buddy flavor” defect (√R5), a sensory issue compromising syrup quality. This study employed a multimodal approach combining headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC/MS) and ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) combined with chemometrics to profile volatile and nonvolatile compounds associated with √R5 and regular (REG) maple syrup. HS-SPME-GC/MS identified key volatile markers, including pyrazines and sulfur-containing compounds such as dimethyl disulfide, many of which were previously unreported. UPLC-QTOF-MS/MS investigated nonpolar compounds and provided insights into nonvolatile chemical signatures underlying maple syrup quality. Chemometric and machine learning analyses classified syrup based on metabolite variations, with principal component analysis (PCA) models showing distinct clustering and PCA-support vector machine (PCA-SVM) achieving 93.8% classification accuracy. This study contributes to a comprehensive understanding of the √R5-associated chemical profiles and underscores the potential of our proposed analytical tools to improve quality assurance frameworks and identification of flavor-altering chemical changes. Chromatography ; Chemistry Plant-Derived Bioactive Compounds ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications