jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fujia Dong | Zhaoyang Ma | Ying Xu | Yingjie Feng | Yingkun Shi | Hui Li | Fukang Xing | Guangxian Wang | Zhongxiong Zhang | Weiguo Yi | Songlei Wang
Source: Food Chemistry (article, 2025-01-27)
Field: Chemistry — Analytical Chemistry

Monitoring of veterinary drug residues in mutton based on hyperspectral combined with explainable AI: A case study of OFX Veterinary drug residues in meat seriously harm human health. Rapid and accurate detection of veterinary drug residues is necessary to minimize contamination. Taking ofloxacin (OFX) residues in mutton as an example, the near-infrared hyperspectral imaging combined with explainable AI was used to evaluate the importance of feature wavelengths in the convolutional neural network-stacked sparse auto-encoder (CNN-SSAE) model for chemical properties. Based on this, the qualitative (residue identification-residue level identification) and quantitative detection of OFX residues in mutton was realized. The results showed that the accuracy of CNN-SSAE in identifying residue and residue level of OFX was 100% and 93.65%, respectively, and the correlation coefficients for validation (R<sup>2</sup><sub>P</sub>) in quantitative detection of OFX residue was 0.8980. In addition, SHapley Additive exPlanation (SHAP) values were used to identify feature wavelengths that contribute the most in the CNN-SSAE model, which effectively explained the quality attribute information that spectral and chemical values may improve the predicted results in the model decision process. The reliability of the CNN-SSAE model was evaluated by statistical validation methods (F-test and T-test). Finally, the visualization diagram of OFX content distribution was established. This study provides a method reference for explainability detection of veterinary drug residues. Hyperspectral imaging ; Veterinary drug ; Veterinary Drugs ; Drug ; Veterinary medicine ; Food science ; Chemistry ; Biology ; Medicine ; Chromatography ; Pharmacology ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Pesticide Residue Analysis and Safety
Authors: Wei Deng | Lijun Liu | Xiaohang Li | Yao Huang | Ming Hu | Yafang Zheng | Yuan Yin | Yan Huan | Shuxun Cui | Zhao‐Yan Sun | Jun Jiang | Xiaoniu Yang | Dapeng Wang
Source: Advanced Materials (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites The traditional trial-and-error approach, although effective, is inefficient for optimizing rubber composites. The latest developments in machine learning (ML)-assisted methodologies are also not suitable for predicting and optimizing rubber composite properties. This is due to the dependency of the properties on processing conditions, which prevents the alignment of data collected from different sources. In this work, a novel workflow called the ML-enhanced trial-and-error approach is proposed. This approach integrates orthogonal experimental design with symbolic regression (SR) to effectively extract empirical principles. This combination enables the optimization process to retain the characteristics of the traditional trial-and-error approach while significantly improving efficiency and capability. Using rubber composites as the model system, the ML-enhanced trial-and-error approach effectively extracts empirical principles encapsulated by high-frequency terms in the SR-derived mathematical formulas, offering clear guidance for material property optimization. An online platform has been developed that allows for no-code usage of the proposed methodology, designed to seamlessly integrate into the existing experimental optimization process. Workflow ; Natural rubber ; Process (computing) ; Computer science ; Materials science ; Property (philosophy) ; Composite number ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Force Microscopy Techniques and Applications
Authors: Seyedeh Fatemeh Alavi | Yuxinxin Chen | Yi-Fan Hou | Fuchun Ge | Peikun Zheng | Pavlo O. Dral
Source: The Journal of Physical Chemistry Letters (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one of the most interesting challenges of contemporary computational chemistry. However, the traditional QM methods are costly for this application. Machine learning techniques have emerged as a powerful tool for substituting the traditional QM methods. Universal interatomic potentials (UIPs) hold a particular promise to deliver accurate results at a fraction of the cost of the traditional QM methods, but the performance of UIPs for calculating anharmonic vibrational frequencies remains hitherto unknown. Here we show that despite a known excellent performance of the representative UIP ANI-1ccx for thermochemical properties, it fails for the anharmonic frequencies due to the original unfortunate choice of the activation function. Hence, we recommend evaluating new UIPs on anharmonic frequencies as an additional important quality test. To remedy the shortcomings of ANI-1ccx, we introduce its reformulation ANI-1ccx-gelu with the GELU activation function, which is capable of calculating IR anharmonic frequencies with reasonable accuracy (close to B3LYP/6-31G*). We also show that our new UIP can be fine-tuned to obtain very accurate anharmonic frequencies for some specific molecules but more effort is needed to improve the overall quality of UIP and its capability for fine-tuning. The new UIP will be included as part of our universal and updatable AI-enhanced QM methods (UAIQM) platform and is available together with usage and fine-tuning tutorials in open-source MLatom at https://github.com/dralgroup/mlatom. The calculations can also be performed via a web browser at https://XACScloud.com. Anharmonicity ; Interatomic potential ; Physics ; Statistical physics ; Materials science ; Molecular physics ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Nuclear Physics and Applications
Authors: Mehmet Emin Demir
Source: Polymer Composites (article, 2025-04-01)
Field: Materials Science — Polymers and Plastics

The effect of filler type (tungsten carbide, zinc oxide) and content on the mechanical and wear behavior of jute/flax reinforced epoxy hybrid composites: Experimental and artificial neural network analysis Abstract The application of natural fiber‐reinforced composite (NFRC) is growing rapidly in various industrial sectors, such as automotive, packaging, and construction materials, because of its affordability and excellent mechanical properties. This study investigates the effects of tungsten carbide (WC) and zinc oxide (ZnO) filler content on the mechanical and wear performance of jute/flax‐reinforced hybrid composites. WC filler enhanced the tensile stress of composites more effectively than ZnO filler. Adding 1% WC and 1.5% ZnO enhanced the tensile strength of the unfilled composite by 41% and 24%, respectively. Wear tests conducted under various parameters revealed that increasing the load reduced the coefficient of friction (COF) but increased the wear rate and mass loss for both neat and filled composites. The lowest COF values were observed in WC‐filled composites. It was observed that ZnO and WC‐filled composites exhibited less matrix deformation, pitting, and porosity compared to neat composites. Impact resistance improved with the addition of ZnO and WC fillers, with the impact strength of 1.5% WC‐filled hybrid composites increasing from 4.5 to 10.3 J/m 2 . The experimental wear rate and COF closely matched the predicted values acquired from the model, demonstrating the success of the artificial neural network (ANN) model in precisely estimating COF and wear rate values. Highlights The addition of fillers improved the mechanical properties of hybrid composites. Exceeding optimum filler ratio negatively affects the mechanical properties. Adding fillers has improved the wear resistance of jute/flax composites. Artificial neural network model strongly correlates with experimental results. Infrared spectroscopy analysis validated the interaction between filler and epoxy. Materials science ; Composite material ; Epoxy ; Filler (materials) ; Tungsten carbide ; Zinc ; Metallurgy Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Tsz Wai Ko | Shyue Ping Ong
Source: npj Computational Materials (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Data-efficient construction of high-fidelity graph deep learning interatomic potentials Abstract Machine learning potentials (MLPs) have become an indispensable tool in large-scale atomistic simulations. However, most MLPs today are trained on data computed using relatively cheap density functional theory (DFT) methods such as the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) functional. While meta-GGAs such as the strongly constrained and appropriately normed (SCAN) functional have been shown to yield significantly improved descriptions of atomic interactions for diversely bonded systems, their higher computational cost remains an impediment to their use in MLP development. In this work, we outline a data-efficient multi-fidelity approach to constructing Materials 3-body Graph Network (M3GNet) interatomic potentials that integrate different levels of theory within a single model. Using silicon and water as examples, we show that a multi-fidelity M3GNet model trained on a combined dataset of low-fidelity GGA calculations with 10% of high-fidelity SCAN calculations can achieve accuracies comparable to a single-fidelity M3GNet model trained on a dataset comprising 8 × the number of SCAN calculations. This work provides a pathway to the development of high-fidelity MLPs in a cost-effective manner by leveraging existing low-fidelity datasets. Computer science ; Graph ; Fidelity ; High fidelity ; Artificial intelligence ; Theoretical computer science ; Engineering ; Telecommunications ; Electrical engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Alexa Canchola | Keyuan Li | Kunpeng Chen | Alejandro Borboa-Pimentel | C.J. Chou | R. Rama | Chi‐Yun Chen | Xinyue Chen | Michael Strobel | Jim E. Riviere | Nancy A. Monteiro‐Riviere | Mingxun Wang | Fan Zhang | Zhoumeng Lin | Wei‐Chun Chou
Source: ACS Nano (article, 2025-10-01)
Field: Materials Science — Biomaterials

Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media A comprehensive understanding of protein corona (PC) composition is critical for engineering nanoparticles (NPs) with optimal safety and therapeutic performance, because the PC governs NP pharmacokinetics, biodistribution, and cellular interactions. Yet systematic analyses are hampered by the absence of standardized, richly annotated data sets. Here, we introduce the Protein Corona Database (PC-DB), which compiles data from 83 studies (2000-2024) and integrates 817 NP formulations with quantitative profiles of 2497 adsorbed proteins. The PC-DB exposes pronounced heterogeneity in NP materials (metal 28.8%, silica 22.8%, lipid-based 14.8%), surface modifications, sizes (1-1400 nm), and ζ-potentials (-70 to +70 mV). Subsequent meta-analysis shows that silica, polystyrene, and lipid-based NPs smaller than 100 nm with moderately negative to neutral ζ-potentials preferentially bind the lipoproteins APOE and APOB-100, which are linked to receptor-mediated uptake and enhanced delivery efficiency. In contrast, metal and metal-oxide NPs carrying highly negative surface charge enrich complement component C3, indicating a greater likelihood of immune recognition and clearance. Interpretable machine learning models (LightGBM and XGBoost; ROC-AUC > 0.85) confirm NP size, ζ-potential, and incubation time as the most influential predictors of protein adsorption. These results delineate how physicochemical parameters dictate PC composition and illustrate the power of predictive modeling to guide rational NP design. Nanoparticle ; Corona (planetary geology) ; Component (thermodynamics) ; Surface charge ; Support vector machine ; Silver nanoparticle ; Complement (music) ; Surface modification Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Electrostatics and Colloid Interactions
Authors: Sophie Blundell | Thomas Radford | Idris A. Ajia | Daniel Lawson | Xingzhao Yan | Mehdi Banakar | David J. Thomson | Ioannis Zeimpekis | Otto L. Muskens
Source: ACS Photonics (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Ultracompact Programmable Silicon Photonics Using Layers of Low-Loss Phase-Change Material Sb<sub>2</sub>Se<sub>3</sub> of Increasing Thickness High-performance programmable silicon photonic circuits are considered to be a critical part of next-generation architectures for optical processing, photonic quantum circuits, and neural networks. Low-loss optical phase-change materials (PCMs) offer a promising route toward nonvolatile free-form control of light. Here, we exploit the direct-write digital patterning of waveguides using layers of the PCM Sb<sub>2</sub>Se<sub>3</sub> with thickness values from 20 to 100 nm, demonstrating the scaling of induced optical phase shift with thickness and the ability to strongly increase the effect per pixel for thicker layers. We exploit the excellent refractive index matching between Sb<sub>2</sub>Se<sub>3</sub> and silicon to achieve a low-loss hybrid platform for programmable photonics. A 5-fold reduction in the modulation length of a Mach-Zehnder interferometer is achieved with increasing thickness compared to the 20 nm thin-film Sb<sub>2</sub>Se<sub>3</sub> devices, which decreased to 5 μm in this work. Application of the thicker PCM layers in direct-write digital programming of a multimode interferometer shows a corresponding 3-fold reduction of the number of programmed pixels to below 10 pixels per device. The demonstrated scaling of performance with Sb<sub>2</sub>Se<sub>3</sub> layer thickness is important for establishing the optimum working range for hybrid silicon-Sb<sub>2</sub>Se<sub>3</sub> devices and holds promise for achieving ultracompact, programmable photonic circuits. Materials science ; Silicon ; Photonics ; Optoelectronics ; Phase (matter) ; Silicon photonics ; Optics ; Phase change ; Engineering physics ; Physics Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: Sahil Kohli | Garima Rathee | Indrani Jha | Lakshita Phor | Harsh Sable | Vishal Chaudhary
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Strategically engineering 2D MXene-based advanced adsorbents for sustainable wastewater remediation of dyes The exponential growth of the global population in the digital era has accelerated urbanization and industrialization, leading to severe complex water pollution from the discharge of toxic dyes into aquatic ecosystems. Two dimensional (2D) MXene-based nano-adsorbents recently emerged as promising candidates for developing sustainable wastewater remediation technologies due to their tunable physicochemical properties, including a high negative zeta potential, a large specific surface area, exceptional adsorption capacity, superior electrical and thermal conductivity, hydrophilicity, and rich surface chemistry. Strategic optimization approaches for MXenes, encompassing interlayer spacing modification, surface engineering, stoichiometric tuning, morphological control, bandgap engineering, membrane fabrication, hybridization, and functionalization, have significantly enhanced their adsorptive performance and dye removal efficiency for real-time wastewater treatment applications. This comprehensive review examines the latest advances in MXene-based nano-adsorbent engineering and their implementation as key components in wastewater treatment strategies for efficient dye removal from industrial effluents, providing fundamental insights into dye-MXene interactions to elucidate underlying complex removal mechanisms. The review highlights the sustainable characteristics of MXene-based nano-adsorbents, including their dye removal capacity, regeneration potential, recyclability, catalytic efficiency, and enhanced physicochemical properties, while addressing critical challenges, such as toxicity concerns, biocompatibility issues, and scalability limitations, that currently hinder their translation from the laboratory to the market. Innovative solutions are proposed through the integration of digital-age technologies, particularly artificial intelligence and machine learning approaches, with the implementation of these recommendations facilitating the establishment of MXene-based nano-adsorbents as sustainable alternatives to conventional commercial adsorbents. This aligns with the UN's Sustainable Development Goals and contributes to the principles of One Health, promoting global welfare. MXenes ; Wastewater ; Environmental remediation ; Adsorption ; Waste management ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Engineering ; Organic chemistry ; Contamination MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Advanced Photocatalysis Techniques
Authors: Jingzong He | Zhengkun Ma | Shilin Liu | Yonggen Lü | Qilin Wu
Source: Advanced Functional Materials (article, 2025-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SGs‐CNTs/PAM/CCS Triple Network Hydrogel: Neural Architecture Inspired for Broadband EMI Shielding and Environmental Resilience Abstract Improving traditional multi‐network hydrogels by introducing a low‐density rigid filler network to optimize a single ionic conductivity and overcome the limited electromagnetic properties to produce electromagnetic interference (EMI) shielding hydrogels is a pioneering challenge. Drawing inspiration from the neural network, this research introduces a novel triple‐network (TN) hydrogel. The single‐layer graphenes (SGs) and carbon nanotubes (CNTs) mimic the conductive channels similar to neurons and axons/dendrites, respectively, and assist the dispersion‐lap‐fixation process of the filler by carboxymethyl chitosan (CCS) and polyacrylamide (PAM). This collaborative assembly of SGs‐CNTs, coupled with the presence of water molecules, imparts SGs‐CNTs/CCS/PAM (SCCP) hydrogel with exceptional EMI shielding effectiveness (SE) across the 8.2–26.5 GHz range (X, Ku, and K bands), reaching SE of 42.31, 50.20, and 60.78 dB, respectively. Moreover, the photothermal properties of SGs‐CNTs enable CCS/PAM to heal sections efficiently and recover electromagnetic properties when exposed to near‐infrared (NIR) light. SCCP also boasts a significant depression of the freezing point to −43 °C, achieved through the hydration of LiCl. Boasting diversified manufacturing, self‐healing properties, and exceptional environmental durability, SCCP stands out as an ideal candidate for EMI shielding and shows excellent potential for multifunctional applications in flexible electronics. Materials science ; Resilience (materials science) ; EMI ; Electromagnetic shielding ; Broadband ; Nanotechnology ; Architecture ; Artificial neural network ; Electromagnetic interference ; Composite material ; Computer science ; Telecommunications ; Artificial intelligence Electromagnetic wave absorption materials ; Dielectric materials and actuators ; Graphene research and applications
Authors: Maogang Li | Qi Cai | Tianlong Zhang | Hongsheng Tang | Hua Li
Source: Journal of Chemometrics (article, 2025-02-01)
Field: Chemistry — Analytical Chemistry

Progress of Complex System Process Analysis Based on Modern Spectroscopy Combined With Chemometrics ABSTRACT In recent years, the role of analytical chemistry has undergone a gradual transformation, evolving from a mere participant to a pivotal decision‐maker in process optimisation. This shift can be attributed to the advent of sophisticated analytical instrumentation, which has ushered in a new era of analytical capabilities. This article presents a review of the developments in the application of intelligent analysis techniques, including infrared (IR) spectroscopy, Raman spectroscopy, and laser‐induced breakdown spectroscopy (LIBS), in the processing of complex systems over the past decade. The review provides an introduction to the fundamental principles of these analytical techniques and examines the evolution of their instrumentation to accommodate online process monitoring. The analysis of spectral data in complex system processes represents a fundamental aspect of the attainment of on‐site quality monitoring, process optimisation and control. Accordingly, the review provides a comprehensive overview of the methodologies employed in process chemometrics, encompassing spectral preprocessing, feature selection, modelling techniques, and optimisation strategies for model performance. Furthermore, this article presents a summary of three intelligent spectral analysis tools, namely infrared spectroscopy, Raman spectroscopy, and LIBS, which are widely employed in process simulation, monitoring, optimisation, and control across multiple disciplines, including the environment, energy, biology, and food. The objective of this review is to provide a valuable reference point and guidance for the further promotion and utilisation of spectral intelligent analysis instruments, with the aim of promoting their in‐depth application and development in a greater number of fields. Chemometrics ; Process (computing) ; Process analytical technology ; Computer science ; Analytical Chemistry (journal) ; Biochemical engineering ; Chemistry ; Chromatography ; Machine learning ; Engineering ; Work in process Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yingcong Zhang | Yiran Yang | Zhixin Yin | Lin Huang | Jiayi Wang
Source: iScience (review, 2025-01-07)
Field: Materials Science — Materials Chemistry

Nanozyme-based wearable biosensors for application in healthcare Recent years have witnessed tremendous advances in wearable sensors, which play an essential role in personalized healthcare for their ability for real-time sensing and detection of human health information. Nanozymes, capable of mimicking the functions of natural enzymes and addressing their limitations, possess unique advantages such as structural stability, low cost, and ease of mass production, making them particularly beneficial for constructing recognition units in wearable biosensors. In this review, we aim to delineate the latest advancements in nanozymes for the development of wearable biosensors, focusing on key developments in nanozyme immobilization strategies, detection technologies, and biomedical applications. The review also highlights the current challenges and future perspectives. Ultimately, it aims to provide insights for future research endeavors in this rapidly evolving area. Wearable computer ; Health care ; Nanotechnology ; Biosensor ; Wearable technology ; Computer science ; Materials science ; Political science Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced Sensor and Energy Harvesting Materials