jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Etienne Palos | Eleftherios Lambros | Saswata Dasgupta | Francesco Paesani
Source: The Journal of Chemical Physics (article, 2022-04-07)
Field: Materials Science — Materials Chemistry

Density functional theory of water with the machine-learned DM21 functional The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn-Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. We find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature. Density functional theory ; Energetics ; Cluster (spacecraft) ; Hybrid functional ; Coupled cluster ; Delocalized electron ; Statistical physics ; Function (biology) ; Aqueous solution ; Chemical physics ; Computer science ; Computational chemistry ; Materials science ; Molecule ; Physics ; Thermodynamics ; Chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Qiye Zheng | Divya Chalise | Mingxin Jia | Yuqiang Zeng | Minxiang Zeng | Mortaza Saeidi‐Javash | A. N. M. Tanvir | Gottlieb Uahengo | Sumanjeet Kaur | Javier E. Garay | Tengfei Luo | Yanliang Zhang | Ravi Prasher | Chris Dames
Source: Applied Physics Reviews (article, 2022-05-11)
Field: Materials Science — Materials Chemistry

Structured illumination with thermal imaging (SI-TI): A dynamically reconfigurable metrology for parallelized thermal transport characterization The recent push for the “materials by design” paradigm requires synergistic integration of scalable computation, synthesis, and characterization. Among these, techniques for efficient measurement of thermal transport can be a bottleneck limiting the experimental database size, especially for diverse materials with a range of roughness, porosity, and anisotropy. Traditional contact thermal measurements have challenges with throughput and the lack of spatially resolvable property mapping, while non-contact pump-probe laser methods generally need mirror smooth sample surfaces and also require serial raster scanning to achieve property mapping. Here, we present structured illumination with thermal imaging (SI-TI), a new thermal characterization tool based on parallelized all-optical heating and thermometry. Experiments on representative dense and porous bulk materials as well as a 3D printed thermoelectric thick film (∼50 μm) demonstrate that SI-TI (1) enables paralleled measurement of multiple regions and samples without raster scanning; (2) can dynamically adjust the heating pattern purely in software, to optimize the measurement sensitivity in different directions for anisotropic materials; and (3) can tolerate rough (∼3 μm) and scratched sample surfaces. This work highlights a new avenue in adaptivity and throughput for thermal characterization of diverse materials. Characterization (materials science) ; Materials science ; Metrology ; Raster scan ; Thermal ; Bottleneck ; Raster graphics ; Throughput ; Porosity ; Nanometrology ; Computer science ; Optoelectronics ; Optics ; Nanotechnology ; Composite material ; Computer graphics (images) ; Artificial intelligence ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Peichen Zhong | Tina Chen | Luis Barroso-Luque | Fengyu Xie | Gerbrand Ceder
Source: Physical review. B./Physical review. B (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

An <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>-norm regularized regression model for construction of robust cluster expansions in multicomponent systems We introduce ${\ensuremath{\ell}}_{0}{\ensuremath{\ell}}_{2}$-norm regularization and hierarchy constraints into linear regression for the construction of cluster expansions to describe configurational disorder in materials. The approach is implemented through mixed integer quadratic programming (MIQP). The ${\ensuremath{\ell}}_{2}$-norm regularization is used to suppress intrinsic data noise, while the ${\ensuremath{\ell}}_{0}$-norm is used to penalize the number of nonzero elements in the solution. The hierarchy relation between clusters imposes relevant physics and is naturally included by the MIQP paradigm. As such, sparseness and cluster hierarchy can be well optimized to obtain a robust, converged set of effective cluster interactions with improved physical meaning. We demonstrate the effectiveness of ${\ensuremath{\ell}}_{0}{\ensuremath{\ell}}_{2}$-norm regularization in two high-component disordered rocksalt cathode material systems, where we compare the cross-validation, convergence speed, and the reproduction of phase diagrams, voltage profiles, and Li-occupancy energies with those of the conventional ${\ensuremath{\ell}}_{1}$-norm regularized cluster expansion models. Regularization (linguistics) ; Norm (philosophy) ; Quadratic equation ; Combinatorics ; Mathematics ; Cluster (spacecraft) ; Algorithm ; Discrete mathematics ; Computer science ; Geometry ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Jee Hyun Seong | Chi Wang | Bren Phillips | Matteo Bucci
Source: International Journal of Heat and Mass Transfer (article, 2022-02-05)
Field: Materials Science — Materials Chemistry

Separate effect of oxidation on the subcooled flow boiling performance of Zircaloy-4 at atmospheric pressure Subcooling ; Boiling ; Materials science ; Wetting ; Atmospheric pressure ; Thermodynamics ; Critical heat flux ; Nucleate boiling ; Surface roughness ; Heat flux ; Heat transfer ; Composite material ; Meteorology Nuclear Materials and Properties ; Heat Transfer and Boiling Studies ; Nuclear Engineering Thermal-Hydraulics
Authors: Juan Lobos | Saravana Prakash Thirumuruganandham | Miguel Ángel Rodríguez‐Pérez
Source: Polymers (article, 2022-09-29)
Field: Materials Science — Polymers and Plastics

Density Gradients, Cellular Structure and Thermal Conductivity of High-Density Polyethylene Foams by Different Amounts of Chemical Blowing Agent LDPE (low-density polyethylene) foams were prepared using the improved compression moulding technique (ICM) with relative densities ranging from 0.3 to 0.7 and with different levels of chemical blowing agents (from 1% to 20%). The density gradients, cellular structure and thermal conductivity of the foams were characterized. The density and amount of CBA used were found to have a significant effect on the cellular structure both at the mesoscale (density gradients) and at the microscale (different cell sizes and cell densities). In addition, the thermal conductivity of the samples is very sensitive to the local structure where the heat flux is located. The technique used to measure this property, the Transient Plane Source method (TPS), makes it possible to detect the presence of density gradients. A simple method for determining these gradients based on thermal conductivity data was developed. Materials science ; Microscale chemistry ; Thermal conductivity ; Low-density polyethylene ; Blowing agent ; Composite material ; Relative density ; Polyethylene ; Conductivity ; Thermal diffusivity ; High-density polyethylene ; Microstructure ; Chemistry ; Thermodynamics Polymer Foaming and Composites ; Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies
Authors: Mansour Zaremanesh | Ali Bahrami
Source: Scientific Reports (article, 2022-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multilayer acoustic invisibility cloak based on composite lattice A concentric cylindrical cloak is showed here to achieve the acoustic cloaking phenomenon. The introduced structure consists of MNE layers and water in MNE substrate in the MHz frequency range. Due to avoiding the incoming acoustic waves by the shell, the object can be hidden inside the cylindrical area of any shape. In order to improve the quality of cloaking, we have optimized the desired shell by considering the manufacturing technology. We show that an optimized, acoustic cloak based on composite lattice structure can reduce the scattering of an object more than a 20-layer realization of acoustic cloak based on multilayer cylindrical structure. This design approach can substantially simplify the fabrication of cloaking shells. In this research, to study the acoustic distribution of the desired structure, finite element method (FEM) has been used to analyze the structure in two dimensions and a cloak of natural materials with isotropic properties has been designed using effective medium theory. Cloak ; Cloaking ; Isotropy ; Finite element method ; Shell (structure) ; Metamaterial ; Acoustics ; Materials science ; Scattering ; Composite number ; Lattice (music) ; Fabrication ; Acoustic wave ; Optics ; Computer science ; Physics ; Composite material ; Structural engineering ; Optoelectronics ; Engineering Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Antenna and Metasurface Technologies
Authors: Vicky Caponigro | Federico Marini | Amalia G. M. Scannell | Aoife Gowen
Source: Food Control (article, 2022-09-02)
Field: Chemistry — Analytical Chemistry

Single-drop technique for lactose prediction in dry milk on metallic surfaces: Comparison of Raman, FT – NIR, and FT – MIR spectral imaging This study applies the single drop techniques to compare the efficacy of Raman, FT – NIR, and FT-MIR spectral imaging to quantify lactose concentration in dried whole milk on different metallic surfaces. Drying the samples avoids degradation problems such as water evaporation or oil degradation and scattering due to micelles. Spectral imaging techniques minimise sampling issues while also describing the sample spatial variation. The mean spectra of pre-processed images were used to build PLS regression models to predict lactose concentration. Raman, FT – NIR (5600–3730 cm−1), FT–MIR (3533–600 cm−1) models and the model obtained using the fusion of the three ranges were built independently and compared. This study confirms that is possible to quantify lactose rapidly using spectral imaging without adding standard references: the minimum RMSEP = 2.8 mg/mL (R2 = 0.98) was achieved with FT – MIR spectral imaging. Lactose ; Raman spectroscopy ; Drop (telecommunication) ; Materials science ; Analytical Chemistry (journal) ; Raman scattering ; Spectral imaging ; Chemistry ; Chromatography ; Optics ; Computer science ; Food science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Listeria monocytogenes in Food Safety
Authors: Le Nhan Pham | Tiffany R. Walsh
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting biomolecule adsorption on MoS<sub>2</sub> nanosheets with high structural fidelity A new force field, MoSu-CHARMM, for the description of bio-interfacial structures at the aqueous MoS<sub>2</sub> interface is developed, based on quantum chemical data. The force field describes non-covalent interactions between the MoS<sub>2</sub> surface and a wide range of chemistries including hydrocarbon, alcohol, aldehyde, ketone, carboxylic acid, amine, thiol, and amino acid groups. Density functional theory (DFT), using the vdW-DF2 functional, is employed to create training and validation datasets, comprising 330 DFT binding energies for 21 organic compounds. Development of MoSu-CHARMM is guided by two criteria: (i) minimisation of energetic differences compared to target DFT data and (ii) preservation of the DFT energetic rankings of the different binding configurations. Force-field performance is validated against existing high-quality structural experimental data regarding adsorption of four 26-residue peptides at the aqueous MoS<sub>2</sub> interface. Adsorption free energies for all twenty amino acids in liquid water are calculated to provide guidance for future peptide design, and interpret the properties of existing experimentally-identified MoS<sub>2</sub>-binding peptides. This force field will enable large-scale simulations of biological interactions with MoS<sub>2</sub> surfaces in aqueous media where an emphasis on structural fidelity is prioritised. Biomolecule ; Adsorption ; Fidelity ; Nanotechnology ; High fidelity ; Materials science ; Chemistry ; Computer science ; Engineering ; Physical chemistry ; Telecommunications 2D Materials and Applications ; MXene and MAX Phase Materials ; Machine Learning in Materials Science
Authors: ‪Carlo Bertinetto‬ | Mark Schoot | Martijn Dingemans | Wouter Meeuwsen | L.M.C. Buydens | Jeroen J. Jansen
Source: Food Research International (article, 2022-08-27)
Field: Chemistry — Analytical Chemistry

Influence of measurement procedure on the use of a handheld NIR spectrophotometer The development of portable NIR instruments facilitates widespread use among non-specialists. However, untrained operators may follow non-optimal measurement procedures. This work investigates how different factors in the measurement procedure influence the spectra of pig feed samples produced by SCiO, a handheld NIR. Measurement conditions were studied by means of Design of Experiments and evaluated with analysis of variance - simultaneous component analysis (ANOVA-SCA or ASCA). We quantified and visualized how measurement distance, angle, background lighting, the use of plastic lids and different devices interactively affect the resulting spectra. The samples could be distinguished with 100% accuracy with Partial Least Squares-Discriminant Analysis (PLS-DA) a scanning distance of 0.5 cm. Replication of the experiment with special attention to reproducing the conditions still lead to some differences, which highlights both the challenges in controlling conditions and the importance of considering them. Based on the results, generalizable guidelines for acceptance of spectra were proposed for this case study. Of main importance are performing measurements at distances of 0.5 cm or at least in an environment without background lighting. Overall, the provided guidelines for measurement conditions and a methodology to investigate this for other devices are a key enabler to spreading handheld spectrometry to a non-expert audience. Mobile device ; Linear discriminant analysis ; Computer science ; Partial least squares regression ; Variance (accounting) ; Observational error ; Statistics ; Artificial intelligence ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Ziwei Liu | Jinbao Jiang | Mengquan Li | Deshuai Yuan | Cheng Nie | Yilin Sun | Peng Zheng
Source: Foods (article, 2022-04-16)
Field: Chemistry — Analytical Chemistry

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture content gradients with 39,119 kernels were collected. Then, the data augmentation method called the difference of spectral mean (DSM) was constructed. K-nearest neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used to verify the effectiveness of the data augmentation method on data with two gradients and three gradients. The experimental results show that the data augmentation can effectively reduce the influence of the difference in moisture content on the model identification accuracy. The DSM method has the highest accuracy improvement in 5 varieties of peanut datasets. In particular, the accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study provides a new method for improving the identification accuracy of moldy peanuts and also provides a reference basis for the screening of related foods such as corn, orange, and mango. Hyperspectral imaging ; Support vector machine ; Robustness (evolution) ; Pattern recognition (psychology) ; Artificial intelligence ; Mathematics ; Computer science ; Remote sensing ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Fengnong Chen | Yao Zhang | Jingcheng Zhang | Lianmeng Liu | Kaihua Wu
Source: Remote Sensing (article, 2022-02-15)
Field: Chemistry — Analytical Chemistry

Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and U. virens infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work. Sowing ; Hyperspectral imaging ; Paddy field ; Smut ; Remote sensing ; Environmental science ; Agricultural engineering ; Computer science ; Pesticide ; Agronomy ; Biology ; Artificial intelligence ; Engineering ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Johanne Heitmann Solheim | Boris Zimmermann | Valeria Tafintseva | Simona Dzurendová | Volha Shapaval | Achim Köhler
Source: Molecules (article, 2022-03-15)
Field: Chemistry — Analytical Chemistry

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks. Analyte ; Weighting ; Normalization (sociology) ; Multiplicative function ; Spectral line ; Orthogonality ; Preprocessor ; Infrared spectroscopy ; Chemometrics ; Biological system ; Chemistry ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Mathematics ; Physics ; Physical chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaoming Xue | Zhenan Chen | Haoqi Wu | Handong Gao
Source: Scientific Reports (article, 2022-07-07)
Field: Chemistry — Analytical Chemistry

Identification of Guiboutia species by NIR-HSI spectroscopy Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate, G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis. Hyperspectral imaging ; Support vector machine ; Linear discriminant analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Identification (biology) ; Computer science ; Partial least squares regression ; Near-infrared spectroscopy ; Spectroscopy ; Sample (material) ; Biological system ; Chemistry ; Chromatography ; Machine learning ; Physics ; Biology ; Optics ; Botany Spectroscopy and Chemometric Analyses ; Wood and Agarwood Research ; Spectroscopy Techniques in Biomedical and Chemical Research