jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Margarita Reit | Lu Xu | Jan‐Christoph Zarges | Hans‐Peter Heim
Source: International Polymer Processing (article, 2025-03-01)
Field: Materials Science — Biomaterials

Use of machine learning methods for modelling mechanical parameters of PLA and PLA/native potato starch compound using aging data Abstract Machine learning methods were applied to investigate changes in material properties during degradation, focusing on bio-based PLA and a PLA/native potato starch compound (50 wt%). Sixteen aging conditions involving various temperatures and humidity levels, and aging durations were examined. Characterization of aged samples involved tensile tests, FTIR analysis, weight or density measurements, and injection molding data. These data served as inputs to develop and compare predictive models of mechanical properties like Young’s modulus and elongation at break. Linear and polynomial regression, as well as multilayer perceptron (MLP) models were employed to evaluate their prediction accuracy. The best model accuracy (RMSE = 0.33) was achieved by segregating the dataset by material type and employing linear regression. Notably, employing two independent variables such as temperature and humidity led to high model quality (RMSE = 0.35). Effect diagrams revealed strong alignment between actual and modeled data, highlighting the comparative strengths of each modeling approach. Starch ; Materials science ; Machine learning ; Artificial intelligence ; Biological system ; Composite material ; Food science ; Computer science ; Biology biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Hao Lai | Anhua Xu | Jianliang Zhai | Youjie Zong | Wei Deng | Haoyan Guo | Bowen Guan | Mingfeng Chang | Rui Xiong | Zhenjun Wang
Source: Applied Clay Science (review, 2025-10-09)
Field: Materials Science — Polymers and Plastics

A systematic review of montmorillonite-asphalt composite materials: Green clay mineral-reinforced asphalt for sustainable pavement solutions Asphalt ; Composite number ; Rheology ; Environmentally friendly ; Adsorption ; Montmorillonite ; Sustainability Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation ; Geotechnical and construction materials studies
Authors: Nathan J. Szymanski | Christopher J. Bartel
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Establishing baselines for generative discovery of inorganic crystals Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and benchmark two baseline approaches - random enumeration of charge-balanced prototypes and data-driven ion exchange of known compounds - against four generative techniques based on diffusion models, variational autoencoders, and large language models. Our results show that established methods such as ion exchange are better at generating novel materials that are stable, although many of these closely resemble known compounds. In contrast, generative models excel at proposing novel structural frameworks and, when sufficient training data is available, can more effectively target properties such as electronic band gap and bulk modulus. To enhance the performance of both the baseline and generative approaches, we implement a post-generation screening step in which all proposed structures are passed through stability and property filters from pre-trained machine learning models including universal interatomic potentials. This low-cost filtering step leads to substantial improvement in the success rates of all methods, remains computationally efficient, and ultimately provides a practical pathway toward more effective generative strategies for materials discovery. By establishing baselines for comparison, this work highlights opportunities for continued advancement of generative models, especially for the targeted generation of novel materials that are thermodynamically stable. Generative grammar ; Materials science ; Nanotechnology ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Xueqing Wang | Lan Wei | Fan Li | Zhangmei Hu | Meikun Fan
Source: Analytical Chemistry (article, 2025-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data Fusion Enhanced High-Dimensional SERS Fingerprints Construction via Dual-Wavelength and Multisubstrate Strategy for Precise Wastewater Identification It is well-known that traditional label-free surface-enhanced Raman spectroscopy (SERS) can capture fingerprint information on analyte, providing a foundation for target identification and differentiation. However, the conventional one-dimensional spectral data obtained through traditional SERS methods is insufficient for characterizing samples with complex chemical compositions, such as wastewater, or for tackling more intricate challenges, including tracing pollution sources, where a more comprehensive analytical profile is necessary. Herein, we introduce "SERSynergy", a data-fusion-driven machine learning approach that integrates dual-wavelength and multisubstrate data to generate a holistic SERS fingerprint, which allows for precise and robust wastewater identification. This method leverages complementary spectral features of wastewater samples by collecting a total of 12,000 spectra using four types of noble metal nanoparticles under two excitation wavelengths. A hybrid feature-decision fusion strategy cross-combined spectral features from various conditions to form high-dimensional fingerprints, which were then evaluated using optimized machine learning models and consolidated via probability-level fusion. The "SERSynergy" method demonstrated an identification accuracy of up to 99.67% for wastewater samples. Furthermore, when validated with blind sample testing, the method maintained an accuracy of 96.67%. Overall, the developed approach shows great promise for efficiently and accurately identifying wastewater samples, and it has potential applications in the precise acquisition of spectral features and identity discrimination in complex matrix samples. Chemistry ; Analyte ; Wastewater ; Fingerprint (computing) ; Spectral signature ; Artificial intelligence ; Sensor fusion ; Fusion ; Identification (biology) ; Biological system ; Pattern recognition (psychology) ; Chemometrics ; Computer science ; Chromatography ; Remote sensing ; Engineering Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Bikash Kanungo | Jeffrey Hatch | Paul M. Zimmerman | Vikram Gavini
Source: Science Advances (article, 2025-09-19)
Field: Materials Science — Materials Chemistry

Learning local and semi-local density functionals from exact exchange-correlation potentials and energies Finding accurate exchange-correlation (XC) functionals remains the defining challenge in density functional theory (DFT). Despite 40 years of active development, attaining general purpose chemical accuracy is still elusive with existing functionals. We present a data-driven pathway to learn the XC functional by using the exact density, XC energy, and XC potential. While the exact densities are obtained from accurate configuration interaction (CI), the exact XC energies and XC potentials are obtained via inverse DFT calculations on the CI densities. We demonstrate how simple neural network (NN)-based local density approximation (LDA) and generalized gradient approximation (GGA), trained on just five atoms and two molecules, provide remarkable improvement in total energies and densities. Particularly, the NN-based GGA functional attains similar accuracy as the higher rung SCAN meta-GGA on various thermochemistry datasets. These results underscore the promise of using the XC potential in modeling XC functionals and can pave the way for systematic learning of increasingly accurate XC functionals. Density functional theory ; Thermochemistry ; Simple (philosophy) ; Local-density approximation ; Inverse ; Orbital-free density functional theory ; Hybrid functional ; Inverse problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Shukai Li | Meng Chen | Mingjie Wei | Jinji Cao | Zhaoqin Xu | Jingtao Wang | Yong Wang
Source: Journal of Membrane Science (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Machine learning on solvent transport through covalent organic frameworks enabled by high-throughput simulations Solvent ; Nanofiltration ; Membrane ; Selectivity ; Polar ; Solvent effects ; Flux (metallurgy) ; Solvophobic Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Memory and Neural Computing
Authors: Laura Min Xuan Chai | Ching Kao | Mingyang Wang | Cheng‐Chih Hsu
Source: Analytical Chemistry (article, 2025-01-19)
Field: Chemistry — Spectroscopy

Untargeted Swab Touch Spray-Mass Spectrometry Analysis with Machine Learning for On-Site Breast Surgical Margin Assessment Direct sampling mass spectrometry (MS) has rapidly advanced with the development of ambient ionization MS techniques. Swab touch-spray (TS)-MS has shown promise for rapid clinical diagnostics. However, commercially available swabs are notorious for their high background signals, particularly in the positive ionization mode. Although changes to MS methods or precleaning of the swabs can serve as workarounds, this inherent issue still limits the clinical application of swab TS-MS. In this study, we report the use of the sterile-packaged OmniSwab as an alternative material for untargeted swab TS-MS analysis. As a proof of concept, breast surgical margins were swabbed <i>in vivo</i> during surgeries and analyzed using a compact mass spectrometer within the hospital. Subsequently, various machine learning algorithms were applied to the acquired MS spectra to determine the optimal model for classifying margins as normal or tumor. The Least Absolute Shrinkage and Selection Operator (LASSO) model yielded the highest prediction performance, with accuracies exceeding 90% in both testing and validation data sets. Notably, three out of four surgical margins involved with cancer cells were accurately identified. The entire workflow, from swab TS-MS analysis to margin prediction, can be completed within 5 min with high accuracy, demonstrating the feasibility of swab TS-MS to assist intraoperative decision-making. Mass spectrometry ; Margin (machine learning) ; Chemistry ; Surgical margin ; Chromatography ; Artificial intelligence ; Computer science ; Surgery ; Machine learning ; Medicine ; Resection Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Farzad Alavi | Kamyab Kashmarizad | O. Rahmani
Source: Computational Materials Science (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

From empirical to machine learning potentials: Unraveling the physical properties of grapheneplus —A novel half-auxetic 2D carbon nanosheet Nanosheet ; Auxetics ; Carbon fibers ; Materials science ; Nanotechnology ; Composite material ; Composite number Graphene research and applications ; Graphene and Nanomaterials Applications ; Advancements in Battery Materials
Authors: Aishwarya Tomar | Apurba Chakraborty | Rahul Kumar
Source: IEEE Access (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Biomolecular Detection Using AlN/<i>β</i>-Ga<sub>2</sub>O<sub>3</sub> MOSHEMT: A Machine Learning-Assisted Analytical and Simulation Framework This study explored the potential of an AlN/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta -Ga_{2}O_{3}$ </tex-math></inline-formula> MOSHEMT (Metal Oxide Semiconductor High Electron Mobility Transistor) as a biosensor. An analytical framework was developed to effectively detect biomolecules, with the biosensor operating by modifying electrical parameters in response to changes in the dielectric constant of these biomolecules. The findings demonstrate significant sensitivity to various biomolecules, with Uricase displaying the highest sensitivity. In comparison to the AlGaN/GaN MOSHEMT, the AlN/<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta -Ga_{2}O_{3}$ </tex-math></inline-formula> MOSHEMT showed improved sensitivity in terms of drain current. Additionally, the machine learning (ML) model created for this investigation correlates strongly with the simulation results. It can accurately predict outcomes within its trained range. Implementing the ML model leads to considerable reductions in both computational costs and time for similar simulations. Furthermore, it eliminates empirical adjustments typically required in traditional physics-based models. Computer science ; Materials science Machine Learning in Materials Science
Authors: Yue Zhao | Xiaopeng Cheng | Ran Zhao | Xipeng Li | Jingxin Meng | Shutao Wang
Source: Advanced Science (review, 2025-07-28)
Field: Materials Science — Materials Chemistry

Recent Advances of Microcapsule‐based Intelligent Coatings: From Bioinspired Designs, Material Selections, to Potential Applications Coating technology is widely used in diverse fields such as energy, healthcare, and aerospace because of the advantages of simple fabrication, low cost, stability, and customized functions. However, conventional coatings fall short in meeting the emerging demands due to their single functionality and non-responsiveness. Inspired by natural prototypes, microcapsule-based intelligent coatings (MICs) that exhibit remarkable responsiveness to external stimuli have been developed and used in various fields such as corrosion protection and anti-fouling. Comprehensively understanding the responsive mechanisms and optimization strategies of MICs can maximize their adaptability and flexibility, which are highly desired for their precise preparation and actual applications. Therefore, this review focuses on recent progress of MICs, varying from bioinspired designs, material selections, stimulus responses, and diverse applications, providing more inspiration to design advanced MICs. First, biological prototypes used in the fabrication of MICs are briefly introduced. Then, the material selections and stimulus responses of MICs, as well as their advantages and disadvantages are emphasized. Later, applications are focused according to their functions, including anti-corrosion, anti-fouling, self-lubricating, and temperature regulation. Finally, the scientific challenges and prospects in designing MICs, focusing on the requirements of developing new structures using machine learning and achieving new applications, are highlighted. Adaptability ; Flexibility (engineering) ; Nanotechnology ; Fouling ; Computer science ; Coating ; Aerospace ; Biochemical engineering ; Systems engineering ; Materials science ; Engineering ; Aerospace engineering Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Surface Modification and Superhydrophobicity
Authors: Adrian Venegas-Reynoso | Benoît Creton | Lucia Giarracca | Marion Lacoue-Nègre | Cyril Ruckebusch | Ludovic Duponchel
Source: Energy & Fuels (article, 2025-02-24)
Field: Chemistry — Analytical Chemistry

Oxidation Stability of Hydrocarbons: A Machine-Learning-Based Study Having fluids that are stable over time is important for many applications, particularly sustainable aviation fuels (SAFs) derived from various renewable sources. Being able to understand this characteristic as early as possible during the development of SAFs would facilitate the blending of renewable sources with or without fossil fuels. Oxidation stability, defined as a hydrocarbon’s resistance to reacting with oxygen at near-ambient temperatures, is one of the most important hydrocarbon-stability-related properties. Indeed, the accumulation of byproducts of oxidation reactions may result in system failures. Assessing this property experimentally remains time-consuming; thus developing fast and accurate predictive models becomes relevant and approaches based on machine learning appear as valuable alternatives. The development of quantitative structure–property relationships (QSPRs) is subject to the availability of reference data, and unfortunately, these are currently lacking in the literature. In this study, we built a database containing consistent experimental results from accelerated oxidation tests conducted on diverse pure hydrocarbons─within the carbon atom number range of SAFs─using the PetroOxy/RapidOxy test method, and second, we applied two machine-learning-based techniques (SVM and XGBoost) on the generated data set to derive QSPR-based models. The contribution of techniques such as data augmentation applied to our data set was also investigated and compared to more classical approaches. The best model (RMSEP = 2.7 h) was obtained after log-transforming the reference Induction Period, performing Smart Data Augmentation to enrich the database content, and using XGBoost with linear learners. While the model’s accuracy is not adequate for quantitative predictions, it allows fast and semiquantitative predictions. Computer science ; Stability (learning theory) ; Support vector machine ; Fossil fuel ; Set (abstract data type) ; Property (philosophy) ; Data set ; Machine learning ; Biochemical engineering ; Artificial intelligence ; Chemistry ; Organic chemistry Petroleum Processing and Analysis ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Thidarut Laochai | Hirotomo Nishihara | Jiaqian Qin | Manunya Okhawilai | Joseph Wang | Nadnudda Rodthongkum | Wiwittawin Sukmas
Source: Sensors and Actuators B Chemical (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Dual-mode sweat urea sensor based on Ti3C2Tx MXene/CuO nanocomposite: Colorimetric-electrochemical detection optimized by machine learning and genetic algorithms Urea ; Wearable computer ; Genetic algorithm ; Random forest ; Electrode ; Electrochemical gas sensor MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing
Authors: Navid Keshmiri | Parisa Najmi | Abbas S. Milani | Mohammad Arjmand
Source: Carbon (article, 2025-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrospinning for electromagnetic interference shielding: Principles, challenges, and future directions Electrospinning is an electrohydrodynamic process in which a liquid droplet is electrified to generate a charged jet that undergoes stretching and elongation to form fibers. This technique is widely recognized for fabricating nonwoven wearable textiles, with promising applications in electromagnetic interference (EMI) shielding for healthcare and military systems. Effective EMI shields depend largely on electrical conductivity; however, electrospinning faces significant challenges when processing conductive materials due to excessive charge dissipation, jet instability, and unintended electrospraying instead of fiber formation. Here, we critically examine these challenges to elucidate the relationship between electrical conductivity and electrospinnability, identifying key bottlenecks in the field. Additionally, the recent progress in transitioning from reflection-based electrospun EMI shields to absorption-dominant ones is discussed in detail. Finally, we outline future directions that include strategies for absorption-dominant shielding, highlight the synergistic potential of electrospinning and electrospraying for scalable production, and advocate for the integration of machine learning tools to accelerate the design of next-generation EMI shielding materials. This review aims to bridge the gap between fundamental research and real-world applications, addressing critical challenges and paving the way toward high-performance, wearable EMI shielding technologies. EMI ; Electrospinning ; Electromagnetic shielding ; Electromagnetic interference ; Wearable computer ; Electrical conductor ; Shields ; Jet (fluid) ; Scalability Electromagnetic wave absorption materials ; Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Jincheng Yue | Jiongzhi Zheng | Xingchen Shen | Krishnendu Maji | Chun‐Chuen Yang | Shuyao Lin | Pierric Lemoine | Emmanuel Guilmeau | Yanhui Liu | Tian Cui
Source: Small (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Diffuson‐Dominated Thermal Transport Crossover From Ordered to Liquid‐Like Cu <sub>3</sub> BiS <sub>3</sub> : The Negligible Role of Ion Hopping Fundamentally understanding lattice dynamics and thermal transport behavior in liquid-like, partially occupied compounds remains a long-standing challenge in condensed matter physics. Here, the microscopic mechanisms are investigated underlying the ultralow thermal conductivity in ordered/liquid-like Cu<sub>3</sub>BiS<sub>3</sub> by combining experimental methods with first-principles calculations. The ordered structure and liquid-like are first experimentally synthesized and characterized, partially Cu-atom occupied Cu<sub>3</sub>BiS<sub>3</sub> structure with increasing temperature. Selfconsistent phonon calculations are then combined, including bubble-diagram corrections, with the Wigner transport equation, considering both phonon propagation and diffuson contributions, to evaluate the anharmonic lattice dynamics and thermal conductivity in phase-change Cu<sub>3</sub>BiS<sub>3</sub>. The theoretical model predicts an ultralow thermal conductivity of 0.34 W m<sup>-1</sup> K<sup>-1</sup> at 400 K, dominated by diffuson contributions, which accurately reproduces and explains the experimental data. Importantly, the machine-learning-based molecular dynamics (MD) simulations not only reproduced the partially Cu-atom occupied Cu<sub>3</sub>BiS<sub>3</sub> structure with the space group Pnma but also successfully replicated the thermal conductivity obtained from experiments and Wigner transport calculations. This observation highlights the negligible impact of ionic mobility arising from partially occupied Cu sites on the thermal conductivity in diffuson-dominated thermal transport compounds. This work sheds light on the minimal impact of ionic mobility on ultralow thermal conductivity in phase-change materials. It demonstrates that the Wigner transport equation accurately describes thermal transport behavior in partially occupied phases with diffuson-dominant thermal transport. Thermal conductivity ; Anharmonicity ; Phonon ; Condensed matter physics ; Molecular dynamics ; Materials science ; Lattice (music) ; Thermal ; Thermodynamics ; Chemical physics ; Physics ; Chemistry ; Computational chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Phase-change materials and chalcogenides