jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ke Xu | Ting Liang | Nan Xu | Penghua Ying | Shunda Chen | Ning Wei | Jianbin Xu | Zheyong Fan
Source: npj Computational Materials (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water’s thermodynamic and transport properties The complex interatomic interactions and strong nuclear quantum effects in water pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across varied conditions. While machine-learned potentials have improved the prediction of either static or transport properties individually, a unified computational framework that accurately captures both has remained elusive. Here, we introduce a machine-learned framework with a highly accurate and efficient neuroevolution potential trained on extensive many-body polarization reference data approaching coupled-cluster-level accuracy, combined with path-integral molecular dynamics and quantum-correction techniques. By capturing the quantum nature of water, this framework accurately predicts its structural, thermodynamic, and transport properties across a broad temperature range, enabling fast, accurate, and simultaneous prediction of self-diffusion coefficient, viscosity, and thermal conductivity. This work represents a major stride in water modeling, providing a unified and robust approach for exploring water’s thermodynamic and transport properties, with broad applications across multiple scientific disciplines. Computer science ; Statistical physics ; Physics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Phase Equilibria and Thermodynamics
Authors: You Zhou | Hideki Hyuga | Yuki Nakashima | Kiyoshi Hirao | Tatsuki Ohji | Manabu Fukushima
Source: Journal of the American Ceramic Society (article, 2025-06-10)
Field: Materials Science — Ceramics and Composites

Effects of rare‐earth oxides on microstructure, thermal conductivity, and mechanical properties of silicon nitride Abstract For the purpose of systematically studying the effects of rare‐earth (RE) oxide additives on the microstructure and properties of silicon nitride ceramics, all the 16 members of the RE family (RE = La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Y, Er, Tm, Yb, Lu, and Sc) were employed in parallel in the present study. An amount of 2 mol% of each RE oxide, in combination of 5 mol% of MgO, was used as additives for a high purity silicon powder in preparing Si 3 N 4 ceramics by the sintering of reaction‐bonded silicon nitride method. Full nitridation was achieved for all the 16 samples. A post‐sintering at 1900°C for 3 h resulted in complete densification, except for the Eu‐bearing sample whose densification was hindered by the large weight loss occurring during sintering. The effects of the 16 RE oxides on the microstructural characteristics (grain diameter, grain length, and grain aspect ratio) and the materials properties (thermal conductivity, bending strength, and fracture toughness) were examined for the sintered samples. While a clear correlation between the grain growth behavior or properties and the RE ionic size was often not found, a rough trend can occasionally be observed. It was found that some materials achieved remarkable properties. For example, the material doped with Sc 2 O 3 attained a high thermal conductivity of 122 W m −1 K −1 ; while fracture toughness of the material doped with Sm 2 O 3 was as high as 10.36 MPa m 1/2 ; the Si 3 N 4 doped with Tm 2 O 3 showed a good balance between the thermal conductivity, fracture strength and fracture toughness (118 W m −1 K −1 , 748 MPa, and 9.23 MPa m 1/2 , respectively). Materials science ; Sintering ; Microstructure ; Silicon nitride ; Fracture toughness ; Ceramic ; Grain size ; Silicon ; Grain growth ; Nitride ; Thermal conductivity ; Flexural strength ; Composite material ; Oxide ; Mineralogy ; Metallurgy ; Chemistry ; Layer (electronics) Advanced ceramic materials synthesis ; MXene and MAX Phase Materials ; Semiconductor materials and devices
Authors: Jing Tan | Peng Zhang | Xiaoqing Song | Chunmiao Han | Feng Wang | Jing Zhang | Chunbo Duan | Zhilong Zhang | Sanyang Han | Hui Xu | Xiaogang Liu
Source: Nature (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Electro-generated excitons for tunable lanthanide electroluminescence Electroluminescence ; Lanthanide ; Nanocrystal ; Exciton ; Luminescence ; Intersystem crossing ; Dopant ; Acceptor Lanthanide and Transition Metal Complexes ; Luminescence Properties of Advanced Materials ; Organic Light-Emitting Diodes Research
Authors: Benjamin A. Jasperson | Ilia Nikiforov | Amit Samanta | Fei Zhou | Ellad B. Tadmor | Vincenzo Lordi | Vasily V. Bulatov
Source: npj Computational Materials (article, 2025-01-04)
Field: Materials Science — Materials Chemistry

Cross-scale covariance for material property prediction A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~108 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤102 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study. Covariance ; Scale (ratio) ; Range (aeronautics) ; Regression ; Statistical physics ; Computer science ; Covariant transformation ; Statistics ; Mathematics ; Physics ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Ion-surface interactions and analysis
Authors: Wenhao Yin | Chong Liu | Jingmin Li
Source: Energy & environment materials (review, 2025-04-11)
Field: Materials Science — Materials Chemistry

A Review on High‐Efficiency Transfer of Graphene Films Free from Defects and Contamination Graphene, owing to its exceptional electronic, optical, thermal, and mechanical properties, has emerged as a highly promising material. Currently, the synthesis of large‐area graphene films on metal substrates via chemical vapor deposition remains the predominant approach for producing high‐quality graphene. To realize the potential applications of graphene, it is essential to transfer graphene films to target substrates in a manner that is non‐destructive, clean, and efficient, as this significantly affects the performance of graphene devices. This review examines the current methods for graphene transfer from three perspectives: non‐destructive transfer, clean transfer, and high‐efficiency transfer. It analyzes and compares the advancements and limitations of various transfer techniques. Finally, the review identifies the key challenges faced by current graphene transfer methods and anticipates future developmental prospects. Contamination ; Graphene ; Materials science ; Nanotechnology ; Biology ; Ecology Graphene research and applications ; Graphene and Nanomaterials Applications ; Advanced Memory and Neural Computing
Authors: Lei Zhang | Hongbo Wang | Qiang Jiang | Lianfu Han | Xuedian Zhang | Songlin Zhuang
Source: Nanophotonics (article, 2025-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual‐wavelength multiplexed metasurface holography based on two‐photon polymerization lithography Two-photon polymerization (TPP) lithography can process 3D micro-nano structures with high precision and has wide applications in the fields of micro-optics. Metasurfaces can flexibly control electromagnetic fields at subwavelength scale, achieving functions such as multidimensional multiplexing holography and achromatic imaging. Meta-devices are usually fabricated via EBL-based process, which is complex and difficult to fabricate meta-devices composed of meta-atoms with different heights. Here, we design a color dual-wavelength metasurface hologram without spatial multiplexing. By combining the propagation phase and the geometric phase, the phase response of two wavelengths is achieved in the same polarization state, and the metasurface is prepared using TPP 3D laser printing technology. The experimentally reconstructed images are consistent with theoretical predictions. This not only verifies the feasibility of this 3D printing technology in the preparation of metasurface samples operating in visible band but also provides potential applications in holographic display, optical encryption, anticounterfeiting, and other fields. Holography ; Achromatic lens ; Lithography ; Optics ; Materials science ; Multiplexing ; Optoelectronics ; Wavelength ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Liquid Crystal Research Advancements
Authors: Ana Amić | Denisa Cagardová | Žiko Milanović
Source: International Journal of Molecular Sciences (article, 2025-01-05)
Field: Chemistry — Organic Chemistry

Theoretical Study of Antioxidant and Prooxidant Potency of Protocatechuic Aldehyde In this study, the antioxidant and prooxidant potency of protocatechuic aldehyde (PCA) was evaluated using density functional theory (DFT). The potency of direct scavenging of hydroperoxyl (HOO<sup>•</sup>) and lipid peroxyl radicals (modeled by vinyl peroxyl, H<sub>2</sub>C=CHOO<sup>•</sup>) involved in lipid peroxidation was estimated. The repair of oxidative damage in biomolecules (lipids, proteins and nucleic acids) and the prooxidant ability of PCA phenoxyl radicals were considered. The repairing potency of PCA was investigated for damaged tryptophan, cysteine, leucine, DNA base guanine and linolenic acid. The thermodynamics and kinetics of the single electron transfer (SET) and formal hydrogen atom transfer (fHAT) mechanisms underlying the studied processes were investigated under physiological conditions in aqueous and lipid environments using the SMD/M06-2X/6-311++G(d,p) level of theory. Sequestration of catalytic Fe<sup>2+</sup> and Fe<sup>3+</sup> ions by PCA, which prevents HO<sup>•</sup> production via Fenton-like reactions, was modeled. Molecular docking was used to study the inhibitory capability of PCA against xanthine oxidase (XO), one of the enzymes producing reactive oxygen species. The attained results show that PCA has the capability to scavenge lipid peroxyl radicals, repair damaged tryptophan, leucine and guanine, chelate catalytic iron ions and inhibit XO. Thus, PCA could be considered a possible multifunctional antioxidant. Chemistry ; Lipid peroxidation ; Antioxidant ; Radical ; Reactive oxygen species ; Hydroperoxyl ; Biochemistry Free Radicals and Antioxidants ; Antioxidant Activity and Oxidative Stress ; Phytochemicals and Antioxidant Activities
Authors: Mahmoud Omar Sobeih | Shadi Sawalha | Rinad Hamed | Fathilah Ali | Minsoo P. Kim
Source: Materials (review, 2025-04-11)
Field: Materials Science — Biomaterials

Starch-Derived Bioplastics: Pioneering Sustainable Solutions for Industrial Use The use of plastics has increased due to the increase in population and applications in various industries. However, fossil fuel-based plastics have caused environmental issues and health hazards due to their non-degradable behavior. To resolve the on-going crisis of these non-degradable polymers, biopolymers have been considered as potential substitutes. Starch is being researched as a polymer matrix to develop bioplastics. Starch is abundant, but due to its poor water barrier and mechanical properties, other materials need to be incorporated in the matrix to improve the material properties. Natural fillers, plasticizers, essential oils, nanoparticles, or polymer blends are materials that can be used in starch-based bioplastics. Adding these materials enhances the mechanical and barrier properties. This review summarizes the recent developments in starch-based bioplastics and biocomposites and discusses the types of starch used, fillers, essential oils, and nanoparticles, explaining how they improve the mechanical, barrier, antibacterial, and biodegradability properties. Furthermore, many of the research products show potential to be used in industrial applications like packaging and agriculture. This review also discusses the potential of starch bioplastics in industrial applications like packaging, automotive applications, biomedical applications, electronics, construction, textiles, and consumer goods. This review also discusses the environmental impact of starch-derived bioplastic products, the life cycle, biodegradation, and recycling process. The circular economy of bioplastics, the economic feasibility of large-scale products, and regulation were also discussed, along with their challenges and the future perspectives of starch-based bioplastics. Bioplastic ; Biodegradation ; Starch ; Materials science ; Biochemical engineering ; Polymer science ; Waste management ; Engineering ; Chemistry ; Food science ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites
Authors: Marcelo Rodrigues Barbosa Júnior | Regimar Garcia dos Santos | Lucas de Azevedo Sales | Rodrigo Vargas | Angelos Deltsidis | Luan Pereira de Oliveira
Source: Heliyon (article, 2025-01-27)
Field: Chemistry — Analytical Chemistry

Image-based and ML-driven analysis for assessing blueberry fruit quality The assessment of blueberry fruit quality is traditionally conducted through laboratory equipment. Despite its high accuracy, this method remains destructive, labor-intensive, time-consuming, and costly. Consequently, there is a pressing need for innovative solutions such as image-based and artificial intelligence (AI)-driven analysis. To address these limitations, this study aimed to analyze whether an approach based on mobile image-based analysis combined with machine learning (ML) algorithms could develop a non-destructive framework for evaluating blueberry fruit quality, specifically focusing on total soluble solids (TSS) and firmness. Firstly, we collected numerous blueberry samples during the maturity stage to construct a comprehensive dataset. These samples were meticulously analyzed in a laboratory for diameter, TSS, firmness, and color. Simultaneously, RGB images were captured using a mobile device. These images were processed to extract spectral bands (red, green, and blue). Eight ML algorithms were employed to develop predictive models capable of predicting the qualitative parameters of the blueberries. Initially, correlation analysis demonstrated that RGB images suggestively contribute to fruit quality assessment (r < 0.41). However, the integration of ML algorithms significantly enhanced the predictive accuracy of these models (R<sup>2</sup> = 0.71-0.99, MAE = 0.003-0.28, and RMSE = 0.004-0.31). This innovative approach differs from existing methods by providing a non-destructive, cost-effective, and efficient alternative to assess blueberry fruit quality. Our findings, therefore, confirm the applicability of mobile images for supporting high-quality blueberry harvesting. These advancements can potentially reduce labor costs, increase yield quality, and support advancements in precision agriculture. Quality (philosophy) ; Philosophy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Horticultural and Viticultural Research
Authors: Guang Zhang | Zhenyang Wang | Shiwei Chen | Zhaoying Xi | Chao Wu | Haizheng Hu | Zeng Liu | Fengmin Wu | Shunli Wang | Zhilai Fang | Weihua Tang | Daoyou Guo
Source: Laser & Photonics Review (article, 2025-06-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of Highly Anisotropic Large‐Area <i>β</i>‐Ga<sub>2</sub>O<sub>3</sub> Thin Films via Crystal Orientation Engineering for Multifunctional Solar‐Blind Polarization‐Sensitive Photodetectors Abstract Solar‐blind polarization‐sensitive photodetector exhibits high selectivity for deep UV light, making it valuable for applications in secure communication, fast radio burst monitoring, industrial material inspection, and so on. β ‐Ga 2 O 3 is particularly well‐suited for photodetector applications due to its optimal optical bandgap and low‐symmetry crystal structure, which provides strong photoelectric anisotropy. However, most heteroepitaxial β ‐Ga 2 O 3 thin films exhibit isotropic properties due to symmetry inheritance from substrates during heteroepitaxy, fundamentally limiting polarization‐sensitive photodetector development. By exploiting the crystallographic‐orientation‐dependent growth via plasma‐enhanced chemical vapor deposition on r ‐plane sapphire substrates, a significant dichroic absorption ratio is achieved in (201)‐oriented β ‐Ga 2 O 3 films in this work. This intrinsic anisotropy enables polarization discrimination without external optics, yielding a polarization ratio of 6.02 under 254 nm illumination. More importantly, by leveraging the unique properties of the fabricated photodetector, several innovative applications, including polarized solar‐blind UV imaging, secure UV communication, and logic circuits are successfully developed. This substrate‐orientation engineering strategy resolves the longstanding symmetry‐mismatch limitation in heteroepitaxial films, advancing polarization‐sensitive photodetectors. Fabrication ; Materials science ; Photodetector ; Optoelectronics ; Anisotropy ; Polarization (electrochemistry) ; Thin film ; Thin film solar cell ; Optics ; Solar cell ; Nanotechnology ; Physics ; Chemistry Ga2O3 and related materials ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Magdalena Piekutowska | Gniewko Niedbała
Source: Agriculture (article, 2025-02-09)
Field: Chemistry — Analytical Chemistry

Review of Methods and Models for Potato Yield Prediction This article provides a comprehensive overview of the development and application of statistical methods, process-based models, machine learning, and deep learning techniques in potato yield forecasting. It emphasizes the importance of integrating diverse data sources, including meteorological, phenotypic, and remote sensing data. Advances in computer technology have enabled the creation of more sophisticated models, such as mixed, geostatistical, and Bayesian models. Special attention is given to deep learning techniques, particularly convolutional neural networks, which significantly enhance forecast accuracy by analyzing complex data patterns. The article also discusses the effectiveness of other algorithms, such as Random Forest and Support Vector Machines, in capturing nonlinear relationships affecting yields. According to standards adopted in agricultural research, the Mean Absolute Percentage Error (MAPE) in the implementation of prediction issues should generally not exceed 15%. Contemporary research indicates that, through the use of advanced and accurate algorithms, the value of this error can reach levels of even less than 10 per cent, significantly increasing the efficiency of yield forecasting. Key challenges in the field include climatic variability and difficulties in obtaining accurate data on soil properties and agronomic practices. Despite these challenges, technological advancements present new opportunities for more accurate forecasting. Future research should focus on leveraging Internet of Things (IoT) technology for real-time data collection and analyzing the impact of biological variables on yield. An interdisciplinary approach, integrating insights from ecology and meteorology, is recommended to develop innovative predictive models. The exploration of machine learning methods has the potential to advance knowledge in potato yield forecasting and support sustainable agricultural practices. Yield (engineering) ; Agronomy ; Biology ; Materials science Spectroscopy and Chemometric Analyses ; Potato Plant Research ; Smart Agriculture and AI
Authors: Yuyan Zhou | Xinyue Wang | Deyu Zhang | Hanxiao Cui | Xiaorong Tian | Wei Du | Zhenghui Yang | Dongling Wan | Zhiwei Qiu | Chao Liu | Zhi-Cheng Yang | Lizhihong Zhang | Qiusheng Yang | Xuefeng Xu | Wenhao Li | Dong Wang | Haojie Huang | W.Z. Wu
Source: Advanced Science (review, 2025-05-08)
Field: Materials Science — Biomaterials

Precision‐Guided Stealth Missiles in Biomedicine: Biological Carrier‐Mediated Nanomedicine Hitchhiking Strategy Nanodrug delivery systems (NDDS) have demonstrated broad application prospects in disease treatment, prevention, and diagnosis due to several advantages, including functionalization capability, high drug-loading capacity, drug stability protection, and the enhanced permeability and retention (EPR) effect. However, their clinical translation still faces multiple challenges, including rapid clearance by the reticuloendothelial system (RES), poor targeting specificity, and insufficient efficiency in crossing biological barriers. To address these limitations, researchers have developed the biological carrier-mediated nanomedicine hitchhiking strategy (BCM-NHS), which leverages circulating cells, proteins, or bacteria as natural "mobile carriers" to enhance drug delivery. This approach enables nanocarriers to inherit the intrinsic biological properties, endowing them with immune evasion, prolonged circulation, dynamic targeting, biocompatibility, biodegradability, and naturally optimized biological interfaces. Here, a systematic overview of the BCM-NHS is provided. First, the review delves into the methods of nanoparticles (NPs) binding and immobilization, encompassing both the surface-attachment-mediated "backpack" strategy and the encapsulation-based "Trojan horse" strategy. Second, the classification of biological carriers, including both cell-based and non-cell-based carriers, is elucidated. Third, the physical properties and release mechanisms of these nanomaterials are thoroughly described. Finally, the latest applications of BCM-NHS in therapeutic and diagnostic contexts across various disease models including tumor, ischemic stroke, and pneumonia are highlighted. Nanocarriers ; Nanomedicine ; Drug delivery ; Nanotechnology ; Biomedicine ; Drug carrier ; Computer science ; Materials science ; Nanoparticle ; Bioinformatics ; Biology Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Micro and Nano Robotics