jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing Li
Source: Journal of Chemistry (article, 2022-05-29)
Field: Materials Science — Materials Chemistry

Application of Computer-Aided Precious Metal Materials in Electrochemistry of Ceramic Jewelry Design In order to solve the electrochemical application of precious metal materials in ceramic jewelry design, a computer-aided design method of precious metal materials in ceramic jewelry was proposed. First, the potential energy surface analyzed in this paper is composed of the sum of disomy and trisomy, with different proportions of disomy and trisomy. Therefore, in the fitting process, the above two relations can be used to measure the contribution of the two-body term and the three-body term, respectively; secondly, from the viewpoint of lattice dynamics, precious metals are of special significance, because they are simple fcc lattice and large and pure single crystals can be obtained. Therefore, their accurate phonon dispersion curves can be obtained experimentally; finally, the phonon dispersion curves along the highly symmetric vector direction are given, and the calculated results are in good agreement with the experimental data. This shows that the new analytical potential energy surface accurately reproduces the interactions inside these crystals. The new analytical potential energy surfaces of the three noble metals correctly reproduce the macroscopic properties of the system, including elastic modulus, cohesive energy, and phonon dispersion curve, as well as important surface features. For all three systems, the order of the surface energies of the unreconstituted surfaces is (110) &gt; (100) &gt; (111), which is also correct. At the same time, the new potential energy surface gives the correct relaxation behavior of the unreconstituted surface. Phonon ; Ceramic ; Chemistry ; Lattice (music) ; Dispersion (optics) ; Surface energy ; Metal ; Surface (topology) ; Potential energy ; Lattice energy ; Condensed matter physics ; Nanotechnology ; Optics ; Materials science ; Physical chemistry ; Geometry ; Crystal structure ; Crystallography ; Classical mechanics ; Physics Machine Learning in Materials Science ; Diverse Musicological Studies
Authors: Christoph Heintze | Iaroslav Babenko | Jiřina Žáčková Suchanová | Alastair Skeffington | Benjamin M. Friedrich | Nils Kroeger
Source: Research Square (preprint, 2022-05-19)
Field: Materials Science — Biomaterials

The molecular basis for pore pattern morphogenesis in diatom silica Abstract Biomineral forming organisms produce inorganic materials with complex, genetically encoded morphologies that are unmatched by current synthetic chemistry. It is poorly understood which genes are involved in biomineral morphogenesis and how the encoded proteins guide this process. We addressed these questions using diatoms, which are paradigms for the self-assembly of hierarchically meso- and macroporous silica under mild reaction conditions. Proteomics analysis of the intracellular organelle for silica biosynthesis led to identification of new biomineralization proteins. Three of these, dAnk1-3, are largely specific to diatoms and contain a common protein-protein interaction domain (ankyrin repeats), indicating a role in coordinating assembly of the silica biomineralization machinery. Knocking out individual dank genes led to aberrations in silica biogenesis that are consistent with liquid-liquid phase separation as underlying mechanism for pore pattern morphogenesis. Our work provides an unprecedented path for the synthesis of tailored meso- and macroporous silicas using Synthetic Biology. Diatom ; Morphogenesis ; Basis (linear algebra) ; Chemical engineering ; Materials science ; Chemistry ; Biology ; Ecology ; Engineering ; Mathematics ; Geometry ; Biochemistry ; Gene Diatoms and Algae Research ; Paleontology and Stratigraphy of Fossils ; Protist diversity and phylogeny
Authors: Jonathan He | Olivia Liu | Xuan Guo
Source: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (article, 2022-12-06)
Field: Chemistry — Spectroscopy

Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics. Computer science ; Artificial intelligence ; Shotgun proteomics ; Heuristics ; Tandem mass spectrometry ; Deep learning ; Pattern recognition (psychology) ; Biomarker discovery ; Proteomics ; Feature (linguistics) ; Mass spectrometry ; Machine learning ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Chenjie Chang | Zongyuan Li | Hongyi Li | Zhuoya Hou | Enguang Zuo | Deyi Zhao | Xiaoyi Lv | Furu Zhong | Cheng Chen | Feng Tian
Source: Scientific Reports (article, 2022-12-10)
Field: Chemistry — Analytical Chemistry

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian. Computer science ; Artificial intelligence ; Deep learning ; Standardization ; Deep neural networks ; Machine learning ; Artificial neural network ; Profit (economics) ; Algorithm ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Haotian Que | Xin Zhao | Xiulan Sun | Qibing Zhu | Min Huang
Source: Research Square (preprint, 2022-08-17)
Field: Chemistry — Analytical Chemistry

Identification of wheat kernel varieties based on hyperspectral imaging technology and grouped convolutional neural network with feature intervals Abstract Variety identification of seeds is essential for evaluating seed purity and ensuring crop yields. This study used hyperspectral imaging to determine 8 high-quality wheat kernel varieties in China combined with a deep learning network and spectral wavelength interval selection methods. The hyperspectral images with a spectral range of 400-1000 nm were firstly processed by three kinds of spectral wavelength interval selection methods including interval continuum removal (iCR), backward interval partial least squares (BiPLS), and interval random frog (iRF) for exploring optimal wavelength intervals. Then the convolutional neural network (CNN) classification models in combination with the processed hyperspectral images were developed. In order to extract effective features, two different structures with full convolution (FC) and grouped convolution (GC) were used. The performance of the proposed models was validated with 8000 samples from the 8 varieties of wheat kernels. The results showed that the combination of feature wavelength and grouping convolution enabled classification models effectively to obtain better classification accuracy and improve the inference speed of the model. The iCR-GC CNN model had the highest classification accuracy with a 4.4% increase and the fastest inference speed with a 44.21% reduction by comparing with the reference CNN classification model. Hyperspectral imaging ; Pattern recognition (psychology) ; Kernel (algebra) ; Convolutional neural network ; Artificial intelligence ; Convolution (computer science) ; Interval (graph theory) ; Computer science ; Inference ; Spectral signature ; Mathematics ; Feature (linguistics) ; Feature selection ; Artificial neural network ; Remote sensing Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Oluwatoba Emmanuel Oyeneyin | Nureni Ipinloju | Chiamaka G. Iwegbulam | Abel Kolawole Oyebamiji | Bambo F. Olajide
Source: Organic Communications (article, 2022-07-18)
Field: Chemistry — Organic Chemistry

Prediction of the antiproliferative effects of some benzimidazole-chalcone derivatives against MCF-7 breast cancer cell lines: QSAR and molecular docking studies Cancer remains a threat to human existence owing the high number of deaths associated with it. Combating cancer has continued to garner interest from pharmaceutical companies as the resistance and toxicity issues have been observed with the treatment of known drugs. A series of benzimidazole-chalcone derivatives were obtained from literature and investigated for their ability to inhibit MCF-7 Breast Cancer Cell Lines via QSAR and molecular docking techniques. A QSAR model was generated to establish the relationship between molecular descriptors and bioactivity of benzimidazole-chalcone derivatives. The R 2 , adjusted R 2 , Q 2 and R 2 pred values obtained suffice to deem the model fit and reliable. The binding affinities of the lead compounds were obtained via an extra precision docking procedure at the active site of the human serine/threonine-protein kinase receptor, 3FC2. Molecule 21 showed the closest binding affinity (-9.350 kcal/mol) as compared to doxorubicin (-9.305 kcal/mol), and had the best IC50 as compared to other compounds as reported earlier in literature. In-silico ADME and drug-likeness prediction of all the molecules showed good pharmacokinetic properties, bioavailability and non-toxicity. The model generated here could be used in predicting the bioactivity of novel antiproliferative agents. MCF-7 ; Benzimidazole ; Chalcone ; Quantitative structure–activity relationship ; Docking (animal) ; Cancer cell lines ; Chemistry ; Breast cancer ; Stereochemistry ; Cancer ; Cancer cell ; Human breast ; Organic chemistry ; Medicine ; Internal medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Byungki Ryu | Jaywan Chung | Masaya Kumagai | Tomoya Mato | Yuki Ando | Sakiko Gunji | Atsumi Tanaka | Dewi Yana | Masayuki Fujimoto | Y. Imai | Yukari Katsura | SuDong Park
Source: Research Square (preprint, 2022-10-24)
Field: Materials Science — Materials Chemistry

Best Thermoelectric Efficiency of Ever-Explored Materials Thermoelectric effect ; Thermoelectric materials ; Thermoelectric generator ; Materials science ; Engineering physics ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Heusler alloys: electronic and magnetic properties
Authors: Kourosh Hakhamaneshi | Marcel Nassar | Mariano Phielipp | Pieter Abbeel | Vladimir Stojanović
Source: arXiv (Cornell University) (preprint, 2022-03-29)
Field: Materials Science — Materials Chemistry

Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supervised pretraining approach to learn circuit representations that can be adapted to new circuit topologies or unseen prediction tasks. We hypothesize that if we train a neural network (NN) that can predict the output DC voltages of a wide range of circuit instances it will be forced to learn generalizable knowledge about the role of each circuit element and how they interact with each other. The dataset for this supervised learning objective can be easily collected at scale since the required DC simulation to get ground truth labels is relatively cheap. This representation would then be helpful for few-shot generalization to unseen circuit metrics that require more time consuming simulations for obtaining the ground-truth labels. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties with up to 10x more sample efficiency compared to a randomly initialized model. We further show that we can improve sample efficiency of prior SoTA model-based optimization methods by 2x (almost as good as using an oracle model) via fintuning pretrained GNNs as the feature extractor of the learned models. Computer science ; Artificial neural network ; Graph ; Generalization ; Electronic circuit ; Network topology ; Extractor ; Ground truth ; Artificial intelligence ; Oracle ; Representation (politics) ; Feature (linguistics) ; Node (physics) ; Machine learning ; Theoretical computer science ; Mathematics ; Engineering Machine Learning in Materials Science ; VLSI and FPGA Design Techniques ; Advanced Memory and Neural Computing
Authors: Nguyen Van My
Source: The University of Danang - Journal of Science and Technology (article, 2022-06-30)
Field: Chemistry — Organic Chemistry

The prediction of pKa values for phenolic compounds by the DFT theory The acid dissociation constant is an important parameter that affects the physicochemical properties of molecules in solution. A set of 20 phenolic compounds were used to establish a model to predict values of phenolic compounds. Calculations in aqueous medium were performed with a polarizable continuum solvent model (PCM) and three hybrid DFT functionals (B3LYP, PBE0, ωB97XD and M062X) with the basis set 6-311++G(d,p). The directly calculated value of gives less accurate results with an average absolute error (MAE) of 1.74 units when using the ωB97XD functional and phenol as reference compound. In the case of using statistical correction, the accuracy of is greatly improved. In the case of using statistical correction, the accuracy of is greatly improved with the lowest MAE value of 0.14 units (M062X; ). The calculated results of in this study have the same accuracy as the experimental measurements.The acid dissociation constant is an important parameter that affects the physicochemical properties of molecules in solution. A set of 20 phenolic compounds were used to establish a model to predict values of phenolic compounds. Calculations in aqueous medium were performed with a polarizable continuum solvent model (PCM) and three hybrid DFT functionals (B3LYP, PBE0, ωB97XD and M062X) with the basis set 6-311++G(d,p). The directly calculated value of gives less accurate results with an average absolute error (MAE) of 1.74 units when using the ωB97XD functional and phenol as reference compound. In the case of using statistical correction, the accuracy of is greatly improved. In the case of using statistical correction, the accuracy of is greatly improved with the lowest MAE value of 0.14 units (M062X; ). The calculated results of in this study have the same accuracy as the experimental measurements. Polarizable continuum model ; Chemistry ; Basis set ; Aqueous solution ; Mean absolute error ; Computational chemistry ; Molecule ; Polarizability ; Acid dissociation constant ; Dissociation (chemistry) ; Phenol ; Thermodynamics ; Hybrid functional ; Solvent ; Density functional theory ; Solvation ; Physical chemistry ; Mathematics ; Physics ; Organic chemistry ; Mean squared error ; Statistics Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Ángela García-García | Jesus Vicente de Julián‐Ortiz | Jorge Gálvez | David Font | Carles Ayats | María del Remedio Guna Serrano | Carlos Muñoz-Collado | Rafael Borrás | José M. Villalgordo
Source: International Journal of Molecular Sciences (article, 2022-12-01)
Field: Chemistry — Organic Chemistry

Similarity-Based Virtual Screening to Find Antituberculosis Agents Based on Novel Scaffolds: Design, Syntheses and Pharmacological Assays A method to identify molecular scaffolds potentially active against the Mycobacterium tuberculosis complex (MTBC) is developed. A set of structurally heterogeneous agents against MTBC was used to obtain a mathematical model based on topological descriptors. This model was statistically validated through a Leave-n-Out test. It successfully discriminated between active or inactive compounds over 86% in database sets. It was also useful to select new potential antituberculosis compounds in external databases. The selection of new substituted pyrimidines, pyrimidones and triazolo[1,5-<i>a</i>]pyrimidines was particularly interesting because these structures could provide new scaffolds in this field. The seven selected candidates were synthesized and six of them showed activity in vitro. Computational biology ; Virtual screening ; Mycobacterium tuberculosis ; Selection (genetic algorithm) ; Similarity (geometry) ; Combinatorial chemistry ; Tuberculosis ; Database ; Chemistry ; Computer science ; Biology ; Bioinformatics ; Medicine ; Drug discovery ; Artificial intelligence Synthesis and biological activity ; Tuberculosis Research and Epidemiology ; Multicomponent Synthesis of Heterocycles
Authors: Rochelle D. Ahmed | Husam Sabah Auhim | Harley L. Worthy
Source: Methods in molecular biology (article, 2022-09-15)
Field: Materials Science — Materials Chemistry

Fluorescent Proteins: Crystallization, Structural Determination, and Nonnatural Amino Acid Incorporation Fluorescence ; Fluorescent protein ; Green fluorescent protein ; Amino acid ; Genetic code ; Protein engineering ; Structural biology ; Chemistry ; Biochemistry ; Chromophore ; Protein structure ; Protein tertiary structure ; Computational biology ; Biophysics ; Biology ; Gene ; Organic chemistry Enzyme Structure and Function ; Glycosylation and Glycoproteins Research ; Bacterial Genetics and Biotechnology
Authors: Jasmina Popović | Srdjan Svrzić | M. Tančić Gajić | Slavica B. Maletić | Vladimir Dodevski | Milanka Djiporović-Momčilović | Sanja Krstić | Mladjan Popović
Source: BioResources (article, 2022-09-01)
Field: Chemistry — Organic Chemistry

Light transmittance of mahogany wood treated with 20% hydrogen peroxide solution There is an increased research interest in methods for transparent wood production and its use. Wood transparency could be achieved by its delignification followed by an impregnation process with polymers having proper optical properties. However, delignification processes are mainly time consuming and not environmentally friendly. The possibility of treating mahogany wood (Swietenia macrophylla King) with 20% hydrogen peroxide for 70, 100, 135, and 170 min at 103 °C is presented in this research. According to the treatment duration, lignin content decreased 40 to 94% relative to its initial content in the control samples, whilst the cell structure remained intact. Due to the light scatter effect, caused mainly by wood tissue structure, the direct optical transmittance of treated samples in the visible light spectrum (400 to 800 nm) was less than 40%. Simultaneously, the total optical transmittance of samples treated for 100 and 135 min reached values between 70 to 80% with high values of the haze at approximately 30 and 60%. Optical transmittance in the visible spectrum area of the samples treated for 170 min was from 45 to 80% and the haze from 25 to 45%. Transmittance ; Materials science ; Hydrogen peroxide ; Lignin ; Haze ; Visible spectrum ; Composite material ; Pulp and paper industry ; Chemistry ; Optoelectronics Wood and Agarwood Research ; Wood Treatment and Properties ; Lignin and Wood Chemistry
Authors: Ming Chen | Roi Baer | Eran Rabani
Source: The Journal of Chemical Physics (article, 2022-12-20)
Field: Materials Science — Materials Chemistry

Structure optimization with stochastic density functional theory Linear-scaling techniques for Kohn-Sham density functional theory are essential to describe the ground state properties of extended systems. Still, these techniques often rely on the localization of the density matrix or accurate embedding approaches, limiting their applicability. In contrast, stochastic density functional theory (sDFT) achieves linear- and sub-linear scaling by statistically sampling the ground state density without relying on embedding or imposing localization. In return, ground state observables, such as the forces on the nuclei, fluctuate in sDFT, making optimizing the nuclear structure a highly non-trivial problem. In this work, we combine the most recent noise-reduction schemes for sDFT with stochastic optimization algorithms to perform structure optimization within sDFT. We compare the performance of the stochastic gradient descent approach and its variations (stochastic gradient descent with momentum) with stochastic optimization techniques that rely on the Hessian, such as the stochastic Broyden-Fletcher-Goldfarb-Shanno algorithm. We further provide a detailed assessment of the computational efficiency and its dependence on the optimization parameters of each method for determining the ground state structure of bulk silicon with varying supercell dimensions. Hessian matrix ; Stochastic gradient descent ; Embedding ; Stochastic optimization ; Mathematical optimization ; Scaling ; Mathematics ; Gradient descent ; Computer science ; Applied mathematics ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Advanced Chemical Physics Studies