jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Esther Heid | Markus Fleck | Payal Chatterjee | Christian Schröder | Alexander D. MacKerell
Source: Journal of Chemical Theory and Computation (article, 2019-02-27)
Field: Materials Science — Materials Chemistry

Toward Prediction of Electrostatic Parameters for Force Fields That Explicitly Treat Electronic Polarization The derivation of atomic polarizabilities for polarizable force field development has been a long-standing problem. Atomic polarizabilities were often refined manually starting from tabulated values, rendering an automated assignment of parameters difficult and hampering reproducibility and transferability of the obtained values. To overcome this, we trained both a linear increment scheme and a multilayer perceptron neural network on a large number of high-quality quantum mechanical atomic polarizabilities and partial atomic charges, where only the type of each atom and its connectivity were used as input. The predicted atomic polarizabilities and charges had average errors of 0.023 Å<sup>3</sup> and 0.019 e using the neural net and 0.063 Å<sup>3</sup> and 0.069 e using the simple increment scheme. As the algorithm relies only on the connectivities of the atoms within a molecule, thus omitting dependencies on the three-dimensional conformation, the approach naturally assigns like charges and polarizabilities to symmetrical groups. Accordingly, a convenient utility is presented for generating the partial atomic charges and atomic polarizabilities for organic molecules as needed in polarizable force field development. Polarizability ; Transferability ; Partial charge ; Force field (fiction) ; Chemistry ; Molecule ; Artificial neural network ; Atom (system on chip) ; Atomic physics ; Statistical physics ; Physics ; Computer science ; Quantum mechanics ; Machine learning Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Force Microscopy Techniques and Applications
Authors: Lina Wang | Xiao‐Na Li | Sheng‐Gui He
Source: Science China Materials (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Recent research progress in the study of catalytic CO oxidation by gas phase atomic clusters Catalysis ; Reactivity (psychology) ; Redox ; Gas phase ; Cluster (spacecraft) ; Materials science ; Nanotechnology ; Phase (matter) ; Atom (system on chip) ; Atmosphere (unit) ; Catalytic oxidation ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Physical chemistry ; Organic chemistry ; Computer science ; Physics ; Thermodynamics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Meng Lei | Sijia Sha | Xueyuan Wang | Jia Wang | Xiao Du | Hang Miao | Hui Zhou | Enhe Bai | Jingmiao Shi | Yongqiang Zhu
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Biomaterials

Co-delivery of paclitaxel and gemcitabine <i>via</i> a self-assembling nanoparticle for targeted treatment of breast cancer Multi-functional nanoparticles can be used to improve the treatment index and reduce side effects of anti-tumor drugs. Herein, we developed a kind of multi-functional and highly biocompatible nanoparticle (NP) loaded with folic acid (FA), paclitaxel (PTX) and gemcitabine (GEM) <i>via</i> self-assembly to target cancer cells. The transmission electron microscopy (TEM) results showed that multi-functional FA targeting nanoparticles (MF-FA NPs) exhibited spherical morphology and favorable structural stability in aqueous solution. In addition, NPs (MF-FA NPs and MF NPs) exhibited comparable proliferation inhibition to breast cancer cell 4T1 compared with the pure drug. In <i>in vivo</i> antitumor studies, NPs showed an obviously enhanced anti-tumor efficacy compared with the pure drug. Furthermore, MF-FA NPs displayed higher tumor growth inhibition than MF NPs due to the specific targeting of FA to cancer cells. Consequently, the novel MF-FA NPs could be used as a potential chemotherapeutic formulation for breast cancer therapy. Paclitaxel ; Gemcitabine ; Breast cancer ; Nanoparticle ; Cancer ; Oncology ; Nanotechnology ; Medicine ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Kaitlin M. Gold | Philip A. Townsend | Eric R. Larson | Ittai Herrmann | Amanda J. Gevens
Source: Phytopathology (article, 2019-12-27)
Field: Chemistry — Analytical Chemistry

Contact Reflectance Spectroscopy for Rapid, Accurate, and Nondestructive <i>Phytophthora infestans</i> Clonal Lineage Discrimination Populations of <i>Phytophthora infestans</i>, the oomycete causal agent of potato late blight in the United States, are predominantly asexual, and isolates are characterized by clonal lineage or asexual descendants of a single genotype. Current tools for clonal lineage identification are time consuming and require laboratory equipment. We previously found that foliar spectroscopy can be used for high-accuracy pre- and postsymptomatic detection of <i>P. infestans</i> infections caused by clonal lineages US-08 and US-23. In this work, we found subtle but distinct differences in spectral responses of potato foliage infected by these clonal lineages in both growth-chamber time-course experiments (12- to 24-h intervals over 5 days) and naturally infected samples from commercial production fields. In both settings, we measured continuous visible to shortwave infrared reflectance (400 to 2,500 nm) on leaves using a portable spectrometer with contact probe. We consistently discriminated between infections caused by the two clonal lineages across all stages of disease progression using partial least squares (PLS) discriminant analysis, with total accuracies ranging from 88 to 98%. Three-class random forest differentiation between control, US-08, and US-23 yielded total discrimination accuracy ranging from 68 to 76%. Differences were greatest during presymptomatic infection stages and progressed toward uniformity as symptoms advanced. Using PLS-regression trait models, we found that total phenolics, sugar, and leaf mass per area were different between lineages. Shortwave infrared wavelengths (>1,100 nm) were important for clonal lineage differentiation. This work provides a foundation for future use of hyperspectral sensing as a nondestructive tool for pathovar differentiation. Biology ; Phytophthora infestans ; Lineage (genetic) ; Oomycete ; Blight ; Genotype ; Botany ; Genetics ; Pathogen ; Gene Spectroscopy and Chemometric Analyses ; Plant Pathogens and Resistance ; Plant Pathogens and Fungal Diseases
Authors: Jiacheng Wu | Lili Kong | Jinwei Zhang | Wuyong Chen
Source: Polish Journal of Environmental Studies (article, 2019-03-05)
Field: Materials Science — Biomaterials

Extraction and Properties of Acid-Soluble Collagenand Pepsin-Soluble Collagen from Silver Carp(Hypophthalmichthys molitrix) Scales: PrerequisiteInformation for Fishery Processing Waste Reuse As a waste of the fish processing industry, scales cause the disposal of large amounts of solid waste into the environment. How to turn such waste into a useful product is an important issue. In this study, acid-soluble collagen (ASC) and pepsin-soluble collagen (PSC) were extracted from silver carp (Hypophthalmichthys molitrix) scales using a new acid-enzyme combined method. The results showed that the extraction yields of ASC and PSC were 5.09% and 12.06%, respectively. Then amino acid analysis, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), Fourier transform infrared spectroscopy (FT-IR), circular dichroism (CD) and ultra-sensitive differential scanning calorimetry (US-DSC) were used to study their composition, properties and structure. Amino acid analysis indicated that ASC and PSC were type I collagens with 34% glycine and 20% imino acid (Pro and Hyp). The results of SDS-PAGE and FT-IR analysis showed that ASC and PSC were similar to those of the standard type I collagen. CD indicated that the triple helical structure of both ASC and PSC were still retained. And the transition from triple helix to random coil of ASC and PSC were 34.26C and 34.47C, respectively. For the first time, the collagen yields of silver carp scales were calculated. Meanwhile, their composition, properties and structure were characterized. These results may offer theoretical support for the development of silver carp scales related to reusing technology to control waste. Hypophthalmichthys ; Silver carp ; Pepsin ; Extraction (chemistry) ; Reuse ; Chemistry ; Fishery ; Grass carp ; Food science ; Chromatography ; Pulp and paper industry ; Biochemistry ; Enzyme ; Biology ; Ecology ; Fish <Actinopterygii> Collagen: Extraction and Characterization ; Protein Hydrolysis and Bioactive Peptides
Authors: Xuezhen Hong | Xian‐Shu Fu | Zheng‐Liang Wang | Li Zhang | Xiaoping Yu | Zihong Ye
Source: Journal of Analytical Methods in Chemistry (article, 2019-01-03)
Field: Chemistry — Analytical Chemistry

Tracing Geographical Origins of Teas Based on FT-NIR Spectroscopy: Introduction of Model Updating and Imbalanced Data Handling Approaches This work presents a reliable approach to trace teas' geographical origins despite changes in teas caused by different harvest years. A total of 1447 tea samples collected from various areas in 2014 (660 samples) and 2015 (787 samples) were detected by FT-NIR. Seven classifiers trained on the 2014 dataset all succeeded to trace origins of samples collected in 2014; however, they all failed to predict origins for the 2015 samples due to different data distributions and imbalanced dataset. Three outlier detection based undersampling approaches-one-class SVM (OC-SVM), isolation forest and elliptic envelope-were then proposed; as a result, the highest macro average recall (MAR) for the 2015 dataset was improved from 56.86% to 73.95% (by SVM). A model updating approach was also applied, and the prediction MAR was significantly improved with increase in the updating rate. The best MAR (90.31%) was first achieved by the OC-SVM combined SVM classifier at a 50% rate. Tracing ; Spectroscopy ; Geography ; Computer science ; Data science ; Physics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Michael Wolloch | Gabriele Losi | Mauro Ferrario | Maria Clelia Righi
Source: Scientific Reports (article, 2019-11-19)
Field: Materials Science — Materials Chemistry

High-throughput screening of the static friction and ideal cleavage strength of solid interfaces We present a comprehensive ab initio, high-throughput study of the frictional and cleavage strengths of interfaces of elemental crystals with different orientations. It is based on the detailed analysis of the adhesion energy as a function of lateral, γ(x, y), and perpendicular displacements, γ(z), with respect to the considered interface plane. We use the large amount of computed data to derive fundamental insight into the relation of the ideal strength of an interface plane with its adhesion. Moreover, the ratio between the frictional and cleavage strengths is provided as good indicator for the material failure mode - dislocation propagation versus crack nucleation. All raw and curated data are made available to be used as input parameters for continuum mechanic models, benchmarks, or further analysis. Cleavage (geology) ; Materials science ; Perpendicular ; Nucleation ; Ab initio ; Dislocation ; Composite material ; Mechanics ; Structural engineering ; Computer science ; Chemistry ; Physics ; Geometry ; Fracture (geology) ; Thermodynamics ; Mathematics ; Engineering Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; High Temperature Alloys and Creep
Authors: Krzysztof Schabowicz | Tomasz Gorzelańczyk | Mateusz Szymków
Source: Materials (article, 2019-02-21)
Field: Materials Science — Polymers and Plastics

Identification of the Degree of Degradation of Fibre-Cement Boards Exposed to Fire by Means of the Acoustic Emission Method and Artificial Neural Networks This paper presents the results of research aimed at identifying the degree of degradation of fibre-cement boards exposed to fire. The fibre-cement board samples were initially exposed to fire at various durations in the range of 1⁻15 min. The samples were then subjected to three-point bending and were investigated using the acoustic emission method. Artificial neural networks (ANNs) were employed to analyse the results yielded by the acoustic emission method. Fire was found to have a degrading effect on the fibres contained in the boards. As the length of exposure to fire increased, the fibres underwent gradual degradation, which was reflected in a decrease in the number of acoustic emission (AE) events recognised by the artificial neural networks as accompanying the breaking of the fibres during the three-point bending of the sample. It was shown that it is not sufficient to determine the degree of degradation of fibre-cement boards solely on the basis of bending strength (<i>MOR</i>). Acoustic emission ; Artificial neural network ; Degradation (telecommunications) ; Degree (music) ; Cement ; Materials science ; Identification (biology) ; Composite material ; Environmental science ; Acoustics ; Engineering ; Computer science ; Artificial intelligence ; Botany ; Biology ; Electronic engineering Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Fire effects on concrete materials
Authors: Thomas A. Manz | Taoyi Chen | Daniel J. Cole | Nidia Gabaldon Limas | Benjamin Fiszbein
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

New scaling relations to compute atom-in-material polarizabilities and dispersion coefficients: part 1. Theory and accuracy Polarizabilities and London dispersion forces are important to many chemical processes. Force fields for classical atomistic simulations can be constructed using atom-in-material polarizabilities and C <sub><i>n</i></sub> (<i>n</i> = 6, 8, 9, 10…) dispersion coefficients. This article addresses the key question of how to efficiently assign these parameters to constituent atoms in a material so that properties of the whole material are better reproduced. We develop a new set of scaling laws and computational algorithms (called MCLF) to do this in an accurate and computationally efficient manner across diverse material types. We introduce a conduction limit upper bound and <i>m</i>-scaling to describe the different behaviors of surface and buried atoms. We validate MCLF by comparing results to high-level benchmarks for isolated neutral and charged atoms, diverse diatomic molecules, various polyatomic molecules (<i>e.g.</i>, polyacenes, fullerenes, and small organic and inorganic molecules), and dense solids (including metallic, covalent, and ionic). We also present results for the HIV reverse transcriptase enzyme complexed with an inhibitor molecule. MCLF provides the non-directionally screened polarizabilities required to construct force fields, the directionally-screened static polarizability tensor components and eigenvalues, and environmentally screened C<sub>6</sub> coefficients. Overall, MCLF has improved accuracy compared to the TS-SCS method. For TS-SCS, we compared charge partitioning methods and show DDEC6 partitioning yields more accurate results than Hirshfeld partitioning. MCLF also gives approximations for C<sub>8</sub>, C<sub>9</sub>, and C<sub>10</sub> dispersion coefficients and quantum Drude oscillator parameters. This method should find widespread applications to parameterize classical force fields and density functional theory (DFT) + dispersion methods. Scaling ; Atom (system on chip) ; Dispersion (optics) ; Statistical physics ; Computational chemistry ; Dispersion relation ; Physics ; Materials science ; Mathematics ; Computational physics ; Chemistry ; Quantum mechanics ; Computer science ; Geometry X-ray Diffraction in Crystallography ; High-pressure geophysics and materials ; nanoparticles nucleation surface interactions
Authors: Evgeny Zhvansky | Stanislav I. Pekov | Anatoly Sorokin | Vsevolod Shurkhay | Vasily A. Eliferov | Potapov Aa | Е. Н. Николаев | Igor Popov
Source: Scientific Reports (article, 2019-01-29)
Field: Chemistry — Spectroscopy

Metrics for evaluating the stability and reproducibility of mass spectra In this work, we demonstrate a new approach for assessing the stability and reproducibility of mass spectra obtained via ambient ionization methods. This method is suitable for both comparing experiments during which only one mass spectrum is measured and for evaluating the internal homogeneity of mass spectra collected over a period of time. The approach uses Pearson's r coefficient and the cosine measure to compare the spectra. It is based on the visualization of dissimilarities between measurements, thus leading to the analysis of dissimilarity patterns. The cosine measure and correlations are compared to obtain better metrics for spectra homogeneity. The method filters out unreliable scans to prevent the analyzed sample from being wrongly characterized. The applicability of the method is demonstrated on a set of brain tumor samples. The developed method could be employed in neurosurgical applications, where mass spectrometry is used to monitor the intraoperative tumor border. Homogeneity (statistics) ; Reproducibility ; Mass spectrum ; Mass spectrometry ; Visualization ; Computer science ; Spectral line ; Measure (data warehouse) ; Ambient ionization ; Pattern recognition (psychology) ; Analytical Chemistry (journal) ; Biological system ; Mathematics ; Ionization ; Artificial intelligence ; Data mining ; Chemistry ; Statistics ; Physics ; Chromatography ; Machine learning ; Electron ionization Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: María Luisa Amodio | Muhammad Mudassir Arif Chaudhry | Giancarlo Colelli
Source: Agronomy (article, 2019-12-19)
Field: Chemistry — Analytical Chemistry

Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops Nowadays, consumer awareness of the impact of site of origin and method of production on the quality and safety of foods, and particularly of fresh produce, is driving the research towards developing various techniques to assist present certifications, traceability, and audit procedures. With regard to horticultural produce, consumer preferences have shifted to fruit and vegetables, which are healthy and ecologically produced, and toward processed foods having sustainable or social certifications and with sites of origin clearly reported on the label. Some recent studies demonstrate the potentiality of near infrared (NIR) technology (including hyperspectral imaging) for discriminating fresh and processed horticultural products based on their composition, quality attributes, and origin. These studies principally mention that each biological tissue possesses a fingerprint NIR spectrum, which consists of a unique and characteristic pattern of radiation, distinguishing a particular biological tissue from physically and/or chemically different samples. Particularly, recent studies discriminated apples, wine, wheat kernels, and derived flours based on their geographical origins. Spectral information allowed discrimination among growing methods (organic and conventional) for asparagus and strawberry fruits, and among harvest dates for fennels, table grapes, and artichokes. Moreover, information about freshness and storage days after minimal processing can be obtained. Recent literature and original results will be discussed. From our perspective, present results suggest that these techniques may have a potentiality to increase information about product history, but if and only if the variability captured by the classification models is vast in terms of diverse samples belonging to various cultivars, varieties, harvest times, cultural practices, geographical origins, storage conditions, and maturity stages, while being used as a complementary method to the conventional ones―either to make an initial screening of critical features, or to add to the amount of available information. Lacking the inclusion of these parameters could result in good classification results, but the reliability of the classification in this case would be dubious in terms of assessment of the factor contributing towards correct classification. Hyperspectral imaging ; Traceability ; Quality (philosophy) ; Asparagus ; Biotechnology ; Geography ; Mathematics ; Biology ; Remote sensing ; Horticulture ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: T.F. Flint | Yongle Sun | Qingrong Xiong | M C Smith | J. A. Francis
Source: Scientific Reports (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Phase-Field Simulation of Grain Boundary Evolution In Microstructures Containing Second-Phase Particles with Heterogeneous Thermal Properties Understanding the interaction between complex thermal fields and metallic structures at the meso-scale is crucial for the prediction of microstructural evolution during thermomechanical processing. The competitive growth of crystal grains, driven by thermodynamic forces at the grain boundaries, is one of the most fundamental phenomena in metallurgy and solid state physics. The presence of second phase particles, which act as pinning sites for boundaries, drastically alters the coarsening behaviour of the system; particularly when considering that these particles have different thermal properties to the primary phase. In this work a multi-phase field model, incorporating thermal gradient and curvature driving forces, is used to predict grain growth in a Ti6Al4V alloy system with second phase particle inclusions representative of oxide and carbide precipitates. The multi-phase field framework is fully coupled to the heat equation. The incorporation of the thermal gradient driving force enables the detailed behaviour of the grain boundaries around the particles to be predicted. It is shown that the inclusion of particles with a lower thermal conductivity has a significant influence on the coarsening behaviour of various systems of grains, due to the combined effects of thermal shielding and the generation of thermal gradient driving forces between the boundaries and pinning particles. Materials science ; Grain boundary ; Phase (matter) ; Zener pinning ; Temperature gradient ; Phase field models ; Microstructure ; Thermal conductivity ; Thermal ; Grain growth ; Particle (ecology) ; Curvature ; Condensed matter physics ; Carbide ; Chemical physics ; Composite material ; Pinning force ; Thermodynamics ; Chemistry ; Physics ; Geometry Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Shion Honda | Hirotaka Akita | Katsuhiko Ishiguro | Toshiki Nakanishi | Kenta Oono
Source: arXiv (Cornell University) (preprint, 2019-09-30)
Field: Materials Science — Materials Chemistry

Graph Residual Flow for Molecular Graph Generation Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics. Among these models, invertible flow-based approaches are not fully explored yet. In this paper, we propose a powerful invertible flow for molecular graphs, called graph residual flow (GRF). The GRF is based on residual flows, which are known for more flexible and complex non-linear mappings than traditional coupling flows. We theoretically derive non-trivial conditions such that GRF is invertible, and present a way of keeping the entire flows invertible throughout the training and sampling. Experimental results show that a generative model based on the proposed GRF achieves comparable generation performance, with much smaller number of trainable parameters compared to the existing flow-based model. Residual ; Graph ; Computer science ; Mathematics ; Combinatorics ; Algorithm Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning and Algorithms