jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tiziana Amoriello | Francesco Mellara | Monica Amoriello | Roberto Ciccoritti
Source: Foods (article, 2024-07-19)
Field: Chemistry — Analytical Chemistry

Evaluation of Nutritional Values of Edible Algal Species Using a Shortwave Infrared Hyperspectral Imaging and Machine Learning Technique In recent years, the growing demand for algae in Western countries is due to their richness in nutrients and bioactive compounds, and their use as ingredients for foods, cosmetics, nutraceuticals, fertilizers, biofuels,, etc. Evaluation of the qualitative characteristics of algae involves assessing their physicochemical and nutritional components to determine their suitability for specific end uses, but this assessment is generally performed using destructive, expensive, and time-consuming traditional chemical analyses, and requires sample preparation. The hyperspectral imaging (HSI) technique has been successfully applied in food quality assessment and control and has the potential to overcome the limitations of traditional biochemical methods. In this study, the nutritional profile (proteins, lipids, and fibers) of seventeen edible macro- and microalgae species widely grown throughout the world were investigated using traditional methods. Moreover, a shortwave infrared (SWIR) hyperspectral imaging device and artificial neural network (ANN) algorithms were used to develop multi-species models for proteins, lipids, and fibers. The predictive power of the models was characterized by different metrics, which showed very high predictive performances for all nutritional parameters (for example, R<sup>2</sup> = 0.9952, 0.9767, 0.9828 for proteins, lipids, and fibers, respectively). Our results demonstrated the ability of SWIR hyperspectral imaging coupled with ANN algorithms in quantifying biomolecules in algal species in a fast and sustainable way. Hyperspectral imaging ; Shortwave ; Remote sensing ; Environmental science ; Infrared ; Geography ; Physics ; Optics ; Radiative transfer Spectroscopy and Chemometric Analyses ; Food Industry and Aquatic Biology ; Algal biology and biofuel production
Authors: Doksoo Lee | Lu Zhang | Yue Yu | Wei Chen
Source: Advanced Optical Materials (article, 2024-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Neural Operator Enabled Concurrent Multitask Design for Multifunctional Metamaterials Under Heterogeneous Fields Abstract Multifunctional metamaterials (MMM) bear promise as next‐generation material platforms supporting miniaturization and customization. Despite many proof‐of‐concept demonstrations and the proliferation of deep learning assisted design, grand challenges of inverse design for MMM, especially those involving heterogeneous fields possibly subject to either mutual meta‐atom coupling or long‐range interactions, remain largely under‐explored. To this end, a data‐driven design framework is presented, which streamlines the inverse design of MMMs involving heterogeneous fields. A core enabler is implicit Fourier neural operator (IFNO), which predicts heterogeneous fields distributed across a metamaterial array, thus in general at odds with homogenization assumptions. Additionally, a standard formulation of inverse problem covering a broad class of MMMs is presented, together with gradient‐based multitask concurrent optimization identifying a set of Pareto‐optimal architecture‐stimulus (A‐S) pairs. Fourier multiclass blending is proposed to synthesize inter‐class meta‐atoms anchored on a set of geometric motifs, while enjoying training‐free dimension reduction and built‐it reconstruction. Interlocking the three pillars, the framework is validated for light‐by‐light programmable nanoantenna, whose design involves vast space jointly spanned by quasi‐freeform supercells, maneuverable incident phase distributions, and conflicting figure‐of‐merits (FoM) involving on‐demand localization patterns. Accommodating all the challenges, the framework can propel future advancements of MMM. Metamaterial ; Materials science ; Operator (biology) ; Artificial neural network ; Nanotechnology ; Computer science ; Artificial intelligence ; Optoelectronics Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Antenna and Metasurface Technologies
Authors: Wenting Liu | Zhang Guicheng | Tao Hu | Sansan Shuai | Chaoyue Chen | Songzhe Xu | Wei Ren | Jiang Wang | Zhongming Ren
Source: ACS Applied Materials & Interfaces (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

On-the-Fly Machine Learning Force Field Study of Liquid-Al/Solid-TiB<sub>2</sub> Interfaces Using the on-the-fly machine learning force field, simulations were performed to study the atomic structure evolution of the liquid-Al/solid-TiB<sub>2</sub> interface with two different terminations, aiming to deepen the understanding of the mechanism of TiB<sub>2</sub> as nucleating particles in an aluminum alloy. We conducted simulations using MLFF for up to 100 ps, enabling us to observe the interfacial properties from a deeper and more comprehensive perspective. The nucleation potential of TiB<sub>2</sub> particles is determined by the formation of various ordered structures at the interface, which is significantly influenced by the termination of the TiB<sub>2</sub> (0001) surface. The evolution of the interface during heterogeneous nucleation processes with different terminations is described using structural information and dynamic characteristics. The Ti-terminated surface is more prone to forming quasi-solid regions compared to the B-termination. Analysis of mean square displacement and vibrational density of states indicates that the liquid layer at the Ti-terminated interface is closer in characteristics to a solid compared to the B-terminated interface. We also found that on the TiB<sub>2</sub> (0001) surface different terminations give rise to distinct ordered structures at the interfaces, which is ascribed to their different diffusion abilities. Materials science ; Nucleation ; Chemical physics ; Alloy ; Diffusion ; Interface (matter) ; Surface (topology) ; Aluminium ; Molecular dynamics ; Field (mathematics) ; Crystallography ; Nanotechnology ; Composite material ; Thermodynamics ; Computational chemistry ; Contact angle Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Advanced Materials Characterization Techniques
Authors: Bin Zhu | Nathanael Leung | Brandon Steel | David England | He Yinglong | Andrew London | Hannah Zhang | Michael Gorley | Yiqiang Wang | Mark J. Whiting | Tan Sui
Source: Materials & Design (article, 2024-11-09)
Field: Materials Science — Materials Chemistry

Machine learning powered predictive modelling of complex residual stress for nuclear fusion reactor design • ML model for residual stress prediction is established for nuclear fusion components. • The residual stress heterogeneity is predicted via grain-level microstructures. • Omnidirectional residual stress is predicted considering material anisotropy. • High accuracy of the prediction is achieved with R -squared value of 0.989. Fusion In-vessel components, assembled and maintained using laser welding, one of the most promising techniques, exhibit complex distributions of residual stress, microstructures, and material properties. These residual stresses can compromise structural integrity and lifespan of critical components. Although using advanced experimental measurements can evaluate the residual stress for individual case, extending the measurements to massive number of components are costly and time-consuming. Traditional machine learning (ML) models struggle to account for the heterogeneity and anisotropy of these stress distributions. Here, we develop a novel ML framework based on the Eurofer97 steel, the structural material for in-vessel components. The ML framework is trained on high-resolution residual stress data derived from recently-developed evaluation techniques. Combining with microstructures, the model enables prediction of heterogenous and anisotropic residual stress distribution. It successfully predicts the compressive residual stress in fusion zone (∼−200 MPa) balanced by tensile residual stress in heat affected zone (∼300 MPa), aligning closely with experimental results with the R -squared value of 0.989 and the mean square error of 10 −4 . Unlike experiments that take hours, the ML model provides predictions within seconds. It offers valuable insights into residual stress prediction for various joints, enhancing the reliability and lifetime prediction of in-vessel components. Materials science ; Residual stress ; Fusion ; Nuclear engineering ; Fusion power ; Stress (linguistics) ; Residual ; Systems engineering ; Composite material ; Computer science ; Engineering ; Nuclear physics ; Plasma Fusion materials and technologies ; Nuclear Materials and Properties ; Welding Techniques and Residual Stresses
Authors: Mahmood Mahmoodi‐Eshkaftaki | Mehdi Mahbod | Hamid Reza Ghenaatian
Source: Renewable Energy (article, 2024-02-13)
Field: Chemistry — Analytical Chemistry

Non-destructive estimation of biomass characteristics: Combining hyperspectral imaging data with neural networks Hyperspectral imaging ; Principal component analysis ; Hemicellulose ; Cellulose ; Artificial neural network ; Biological system ; Biomass (ecology) ; Chemistry ; Environmental science ; Analytical Chemistry (journal) ; Environmental chemistry ; Artificial intelligence ; Computer science ; Geology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Phytochemicals and Antioxidant Activities
Authors: Qingyun Cai | Liangzhi Qiao | Shan‐Jing Yao | Dong‐Qiang Lin
Source: Separation and Purification Technology (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Machine learning assisted QSAR analysis to predict protein adsorption capacities on mixed-mode resins Quantitative structure–activity relationship ; Adsorption ; Chemistry ; Mode (computer interface) ; Mixed mode ; Biochemical engineering ; Chromatography ; Computer science ; Engineering ; Organic chemistry ; Materials science ; Stereochemistry ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein purification and stability
Authors: Gechuanqi Pan | Jing Ding | Yecheng Yao | Zhun Yuan | Duu‐Jong Lee
Source: Solar Energy Materials and Solar Cells (article, 2024-08-06)
Field: Materials Science — Materials Chemistry

Thermal performance of MgCl2–NaCl–KCl eutectic salt for the next generation concentrated solar power and correlation between structure and thermophysical properties: Insights from atomic and electronic levels Eutectic system ; Thermal ; Salt (chemistry) ; Concentrated solar power ; Chemistry ; Materials science ; Thermodynamics ; Mineralogy ; Chemical engineering ; Inorganic chemistry ; Thermal energy storage ; Physical chemistry ; Crystallography ; Physics Thermal Expansion and Ionic Conductivity ; Chemical Looping and Thermochemical Processes ; Phase Change Materials Research
Authors: Haibing Zhang | Bichuan Cao | Lei Huang | Xiaogang Peng | Linjun Wang
Source: Nano Research (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Machine learning force field study of carboxylate ligands on the surface of zinc-blende CdSe quantum dots In colloidal quantum dots (QDs), the geometries of surface ligands may play significant roles in tuning the electronic structure, optical spectra and exciton dynamics. We here propose an effective approach to build a diverse dataset of small QDs, based on which the machine learning force field (MLFF) can be obtained based on the DeePMD framework and the energy of each atom is expressed based on the local atomic structure. Using the obtained QD force field (QDFF), molecular dynamics simulation of large zinc-blende CdSe QDs passivated by carboxylate ligands is successfully carried out, and the complex surface structure is extensively studied. We find that bridging, tilted, chelating and claw geometries are the major geometries of carboxylate ligands in CdSe QDs, and the alkyl chain length of ligands plays a significant role. The Markov state model is utilized to reveal the detailed geometry transformation channels. Due to the high performance of QDFF, the present approach is promising for systematic studies of large QDs with different kinds of ligands that can be synthesized in experiment. Quantum dot ; Zinc ; Carboxylate ; Field (mathematics) ; Surface (topology) ; Materials science ; Chemistry ; Condensed matter physics ; Nanotechnology ; Physics ; Stereochemistry ; Mathematics ; Metallurgy ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Sanket Rajan Gupte | Cathy Hou | Gong‐Her Wu | Jesús G. Galaz-Montoya | Wah Chiu | Serena Yeung
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-06-30)
Field: Materials Science — Surfaces, Coatings and Films

CryoViT: Efficient Segmentation of Cryogenic Electron Tomograms with Vision Foundation Models ABSTRACT Cryogenic electron tomography (cryoET) directly visualizes subcellular structures in 3D at the nanometer scale. Quantitative analyses of cryoET data can reveal structural biomarkers of diseases, provide novel mechanistic insights, and inform the effects of treatments on phenotype. However, existing automated annotation approaches primarily focus on localizing molecular features with few methods accurately quantifying complex structures such as organelles. We address this challenge with CryoViT, a paradigm shift from traditional convolutional neural networks that leverages vision transformers to enhance the segmentation of large pleomorphic structures that can occupy almost the entire field of view in high-magnification images, such as mitochondria. CryoViT is powered by a large-scale vision foundation model and overcomes limitations of popular U-Net based methods, particularly when training data are scarce. We demonstrate the efficacy of CryoViT on a large cryoET dataset of neurons differentiated from iPSCs derived from Huntington disease (HD) patients and cultured HD mouse model neurons. Foundation (evidence) ; Segmentation ; Electron tomography ; Tomography ; Electron ; Artificial intelligence ; Computer vision ; Computer science ; Physics ; Optics ; Nuclear physics ; Geography ; Electron microscope ; Archaeology Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Nuclear Physics and Applications
Authors: Jing Liang | Nanying Lv | Zhina Xie | Xiuyuan Yin | Suiyuan Chen | Changsheng Liu
Source: Metals and Materials International (article, 2024-06-16)
Field: Materials Science — Metals and Alloys

Study on the Composition Design, Microstructure, Wear and Corrosion Resistant of Duplex Stainless Steels Based on Machine Learning Corrosion ; Materials science ; Austenite ; Indentation hardness ; Microstructure ; Metallurgy ; Martensite ; Duplex (building) ; Ferrite (magnet) ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses
Authors: Yingjie Lu | Yuqi Cao | Xiaohang Tang | Na Hu | Zhengyong Wang | Peng Xu | Zhendong Hua | Youmei Wang | Yue Su | Yinlong Guo
Source: Talanta (article, 2024-02-07)
Field: Chemistry — Spectroscopy

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances Chemistry ; Synthetic cannabinoids ; Mass spectrometry ; Mass spectrometry imaging ; Mephedrone ; Artificial intelligence ; Pattern recognition (psychology) ; Drug ; Chromatography ; Psychology ; Computer science ; Psychiatry ; Cannabinoid Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Forensic Toxicology and Drug Analysis
Authors: Huimin Yu | Miaomiao Tang | Jian He | Changjun Liu | Pan Wu | Wei Jiang
Source: Journal of Water Process Engineering (article, 2024-05-13)
Field: Chemistry — Inorganic Chemistry

Intelligent treatment of tannery wastewater via H2O2 photocatalytic oxidation coupled adsorption process Wastewater ; Adsorption ; Chromium ; Effluent ; Photocatalysis ; Chemistry ; Pollutant ; Industrial wastewater treatment ; Catalysis ; Nuclear chemistry ; Radical ; Pulp and paper industry ; Environmental science ; Environmental engineering ; Organic chemistry ; Engineering Pigment Synthesis and Properties ; Dye analysis and toxicity ; Water Quality Monitoring and Analysis
Authors: B. Zou | Xiaoyu Zhao | Jung‐Chi Liao | Yue Zhao
Source: Microchemical Journal (article, 2024-12-23)
Field: Chemistry — Analytical Chemistry

Near-infrared activity analysis and identification of fungal and bacterial markers Identification (biology) ; Microbiology ; Infrared ; Computational biology ; Chemistry ; Biology ; Botany ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Junyao Gong | Gang Chen | Yuezhao Deng | Cheng Li | Kui Fang
Source: Agriculture (article, 2024-09-28)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Tea Polyphenols in Fu Brick Tea Based on Hyperspectral Imaging and Improved PKO-SVR Method Tea polyphenols (TPs) are a critical indicator for evaluating the quality of tea leaves and are esteemed for their beneficial effects. The non-destructive detection of this component is essential for enhancing precise control in tea production and improving product quality. This study developed an enhanced PKO-SVR (support vector regression based on the Pied Kingfisher Optimization Algorithm) model for rapidly and accurately detecting tea polyphenol content in Fu brick tea using hyperspectral reflectance data. During this experiment, chemical analysis determined the tea polyphenol content, while hyperspectral imaging captured the spectral data. Data preprocessing techniques were applied to reduce noise interference and improve the prediction model. Additionally, several other models, including K-nearest neighbor (KNN) regression, neural network regression (BP), support vector regression based on the sparrow algorithm (SSA-SVR), and support vector regression based on particle swarm optimization (PSO-SVR), were established for comparison. The experiment results demonstrated that the improved PKO-SVR model excelled in predicting the polyphenol content of Fu brick tea (R2 = 0.9152, RMSE = 0.5876, RPD = 3.4345 for the test set) and also exhibited a faster convergence rate. Therefore, the hyperspectral data combined with the PKO-SVR algorithm presented in this study proved effective for evaluating Fu brick tea’s polyphenol content. Hyperspectral imaging ; Polyphenol ; Brick ; Chemistry ; Artificial intelligence ; Computer science ; Materials science ; Composite material ; Biochemistry Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Food Quality and Safety Studies
Authors: A.A. Potemkin | Мikhail А. Proskurnin | Dmitry S. Volkov
Source: Analytical Chemistry (article, 2024-03-26)
Field: Chemistry — Analytical Chemistry

Noise Filtering Algorithm Using Gaussian Mixture Models for High-Resolution Mass Spectra of Natural Organic Matter High-resolution mass spectra of natural organic matter (NOM) contain a large number of noise signals. These signals interfere with the correct molecular composition estimation during nontargeted analysis because formula-assignment programs find empirical formulas for such peaks as well. Previously proposed noise filtering methods that utilize the profile of the intensity distribution of mass spectrum peaks rely on a histogram to calculate the intensity threshold value. However, the histogram profile can vary depending on the user settings. In addition, these algorithms are not automated, so they are handled manually. To overcome the mentioned drawbacks, we propose a new algorithm for noise filtering in mass spectra. This filter is based on Gaussian Mixture Models (GMMs), a machine learning method to find the intensity threshold value. The algorithm is completely data-driven and eliminates the need to work with a histogram. It has no customizable parameters and automatically determines the noise level for each individual mass spectrum. The algorithm performance was tested on mass spectra of natural organic matter obtained by averaging a different number of microscans (transients), and the results were compared with other noise filters proposed in the literature. Finally, the effect of this noise filtering approach on the fraction of peaks with assigned formulas was investigated. It was shown that there is always an increase in the identification rate, but the magnitude of the effect changes with the number of microscans averaged. The increase can be as high as 15%. Noise (video) ; Histogram ; Mass spectrum ; Filter (signal processing) ; Algorithm ; Gaussian ; Chemistry ; Spectral line ; Gaussian noise ; Biological system ; Pattern recognition (psychology) ; Mass spectrometry ; Artificial intelligence ; Computer science ; Physics ; Image (mathematics) ; Chromatography Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Qing Lü | Chi Ding | Qiuhan Jia | Shuning Pan | Jiuyang Shi | Yu Han | Junjie Wang | Xiaomeng Wang | Dingyu Xing | Jian Sun
Source: Physical review. B./Physical review. B (article, 2024-05-22)
Field: Chemistry — Inorganic Chemistry

Superconducting, plastic, and superionic states driven by four-membered lithium rings in a high-pressure lithium-lead compound Using a combination of crystal structure search methods, first-principles calculations, and machine learning potential based simulations, we explored the lithium-lead system and predicted five phases: ${I}_{4}$/mcm ${\mathrm{LiPb}}_{2}$, Pnma LiPb, ${I}_{4}$/mmm ${\mathrm{Li}}_{4}\mathrm{Pb}, C2/m {\mathrm{Li}}_{5}\mathrm{Pb}$, and Cmcm ${\mathrm{Li}}_{6}\mathrm{Pb}$. Among them, ${I}_{4}$/mmm ${\mathrm{Li}}_{4}\mathrm{Pb}$ displayed remarkable properties, including superconductivity, plasticity, and superionic behavior at varying temperature ranges. At lower temperatures, ${I}_{4}$/mmm ${\mathrm{Li}}_{4}\mathrm{Pb}$ manifests superconductivity with a critical transition temperature of 4--5 K. Its superconducting behavior is attributed to the interplay between the ${B}_{2}$ g vibration mode, which signifies the rotational motion of four-membered lithium rings within the stacking layer, and the participation of $p$-orbital electrons. As temperature rises, ${I}_{4}$/mmm ${\mathrm{Li}}_{4}\mathrm{Pb}$ first transitions into a plastic phase, marked by continuous collective rotation of intralayer four-membered lithium rings, and then shows superionic behavior characterized by the emergence of interlayer lithium atom diffusion. These unique behaviors stem from stronger Li-Li bonds within four-membered lithium rings and a lower energy barrier for collective motion, distinct from interstitial localized electrons in electrides found in other lithium-based systems. This work provides an intriguing platform for exploring distinct states and establishes a correlation between various physical phenomena and the system's structure and bonding. Lithium (medication) ; Lead (geology) ; Superconductivity ; Materials science ; High pressure ; Condensed matter physics ; Engineering physics ; Physics ; Geology ; Psychology Inorganic Chemistry and Materials ; Coordination Chemistry and Organometallics ; Synthesis and characterization of novel inorganic/organometallic compounds