jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xin Chen | Mathias Jørgensen | Jun Li | Bjørk Hammer
Source: Journal of Chemical Theory and Computation (article, 2018-05-29)
Field: Materials Science — Materials Chemistry

Atomic Energies from a Convolutional Neural Network Understanding interactions and structural properties at the atomic level is often a prerequisite to the design of novel materials. Theoretical studies based on quantum-mechanical first-principles calculations can provide this knowledge but at an immense computational cost. In recent years, machine learning has been successful in predicting structural properties at a much lower cost. Here we propose a simplified structure descriptor with no empirical parameters, "k-Bags", together with a scalable and comprehensive machine learning framework that can deepen our understanding of atomic properties of structures. This model can readily predict structure-energy relations that can provide results close to the accuracy of ab initio methods. The model provides chemically meaningful atomic energies enabling theoretical analysis of organic and inorganic molecular structures. Utilization of the local information provided by the atomic energies significantly improves upon the stochastic steps in our evolutionary global structure optimization, resulting in a much faster global minimum search of molecules, clusters, and surfaced supported species. Computer science ; Scalability ; Ab initio ; Convolutional neural network ; Quantum ; Artificial neural network ; Artificial intelligence ; Machine learning ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Jianbo Fu | Jing Tang | Yunxia Wang | Xuejiao Cui | Qingxia Yang | Jiajun Hong | Xiaoxu Li | Shuang Li | Yu Chen | Weiwei Xue | Feng Zhu
Source: Frontiers in Pharmacology (article, 2018-06-26)
Field: Chemistry — Spectroscopy

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique. Normalization (sociology) ; Computer science ; Data mining ; Label-free quantification ; Database normalization ; Data set ; Transformation (genetics) ; Pattern recognition (psychology) ; Computational biology ; Artificial intelligence ; Quantitative proteomics ; Proteomics ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Juan He | Lidan Chen | Bingquan Chu | Chu Zhang
Source: Molecules (article, 2018-09-19)
Field: Chemistry — Analytical Chemistry

Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis The rapid and nondestructive determination of active compositions in <i>Chrysanthemum morifolium</i> (Hangbaiju) is of great value for producers and consumers. Hyperspectral imaging as a rapid and nondestructive technique was used to determine total polysaccharides and total flavonoids content in <i>Chrysanthemum morifolium</i>. Hyperspectral images of different sizes of <i>Chrysanthemum morifolium</i> flowers were acquired. Pixel-wise spectra within all samples were preprocessed by wavelet transform (WT) followed by standard normal variate (SNV). Partial least squares (PLS) and least squares-support vector machine (LS-SVM) were used to build prediction models using sample average spectra calculated by preprocessed pixel-wise spectra. The LS-SVM model performed better than the PLS models, with the determination of the coefficient of calibration (R²c) and prediction (R²p) being over 0.90 and the residual predictive deviation (RPD) being over 3 for total polysaccharides and total flavonoids content prediction. Prediction maps of total polysaccharides and total flavonoids content in <i>Chrysanthemum morifolium</i> flowers were successfully obtained by LS-SVM models, which exhibited the best performances. The overall results showed that hyperspectral imaging was a promising technique for the rapid and accurate determination of active ingredients in <i>Chrysanthemum morifolium</i>, indicating the great potential to develop an online system for the quality determination of <i>Chrysanthemum morifolium</i>. Chrysanthemum morifolium ; Hyperspectral imaging ; Partial least squares regression ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Horticulture ; Computer science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Anees Mohammed | Juan I. Melecio | Siniša Djurović
Source: IEEE Transactions on Industrial Electronics (article, 2018-12-18)
Field: Materials Science — Materials Chemistry

Stator Winding Fault Thermal Signature Monitoring and Analysis by <i>In Situ</i> FBG Sensors Winding short circuit faults are recognized as one of most frequent electric machine failure modes. Effective on-line diagnosis of these is vital, but remains a challenging task, in particular, at incipient fault stage. This paper reports a novel technique for on-line detection of incipient stator short circuit faults in random wound electrical machines based on in situ monitoring of windings thermal signature using electrically nonconductive and electromagnetic interference immune fiber-Bragg grating (FBG) temperature sensors. The presented method employs distributed thermal monitoring, based on the FBG multiplexing feature, in a variety of points within windings, in proximity to thermal hot spots of interest that arise from fault. The ability of the proposed method to enable fault diagnosis through identification of fault-induced localized thermal excitation is validated in steady-state and transient operating conditions on a purpose built inverter driven induction machine test facility. The results demonstrate the capability of unambiguous detection of inter-turn faults, including a single shorted turn. Furthermore, the winding thermal and electrical characteristics at the onset of inter-turn fault are examined and correlated, enabling better understanding of fault diagnostic requirements. Signature (topology) ; Stator ; Fault (geology) ; In situ ; Fault detection and isolation ; Thermal ; Electromagnetic coil ; Electrical engineering ; Electronic engineering ; Computer science ; Materials science ; Engineering ; Physics ; Geology ; Actuator ; Mathematics ; Seismology High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors ; Power Transformer Diagnostics and Insulation
Authors: Mohana Shivanna | Qing‐Yuan Yang | Alankriti Bajpai | Susan Sen | Nobuhiko Hosono | Shinpei Kusaka | Tony Pham | Katherine A. Forrest | Brian Space | Susumu Kitagawa | Michael J. Zaworotko
Source: Science Advances (article, 2018-04-06)
Field: Chemistry — Inorganic Chemistry

Readily accessible shape-memory effect in a porous interpenetrated coordination network Shape-memory effects are quite well-studied in general, but there is only one reported example in the context of porous materials. We report the second example of a porous coordination network that exhibits a sorbate-induced shape-memory effect and the first in which multiple sorbates, N<sub>2</sub>, CO<sub>2</sub> and CO promote this effect. The material, a new threefold interpenetrated pcu network, [Zn<sub>2</sub>(4,4'-biphenyldicarboxylate)<sub>2</sub>(1,4-bis(4-pyridyl)benzene)]<sub>n</sub> (X-pcu-3-Zn-3i), exhibits three distinct phases: the as-synthesized α phase; a denser-activated β phase; and a shape-memory γ phase, which is intermediate in density between the α and β phases. The γ phase is kinetically stable over multiple adsorption/desorption cycles and only reverts to the β phase when heated at >400 K under vacuum. The α phase can be regenerated by soaking the γ phase in <i>N</i>,<i>N</i>'-dimethylformamide. Single-crystal x-ray crystallography studies of all three phases provide insight into the shape-memory phenomenon by revealing the nature of interactions between interpenetrated networks. The β and γ phases were further investigated by in situ coincidence powder x-ray diffraction, and their sorption isotherms were replicated by density functional theory calculations. Analysis of the structural information concerning the three phases of X-pcu-3-Zn-3i enabled us to understand structure-function relationships and propose crystal engineering principles for the design of more examples of shape-memory porous materials. Porosity ; Context (archaeology) ; Desorption ; Materials science ; Phase (matter) ; Density functional theory ; Adsorption ; Shape-memory alloy ; Crystallography ; Sorption ; Porous medium ; Crystal (programming language) ; Chemical engineering ; Chemistry ; Physical chemistry ; Computational chemistry ; Organic chemistry ; Composite material ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Polyoxometalates: Synthesis and Applications
Authors: Thomas R. Durrant | Samuel T. Murphy | Matthew B. Watkins | Alexander L. Shluger
Source: The Journal of Chemical Physics (article, 2018-07-11)
Field: Materials Science — Materials Chemistry

Relation between image charge and potential alignment corrections for charged defects in periodic boundary conditions Charged defects are often studied within the periodic density functional theory (DFT), but this introduces strong finite-size artifacts. In this work, we develop an electrostatic image interaction correction (IIC) method based on the direct solution of the Poisson equation for charge models constructed directly from DFT calculations. These IICs are found to be detail-insensitive, depending almost entirely on bulk dielectric properties. As these IICs are not able to fully explain the observed finite-size scaling, we explore potential alignment in detail and introduce a novel decomposition to separate out different contributions. We find that the two main sources of potential alignment are defect image interactions and changes in the number of atoms present in the supercell. This first effect is accurately predicted by the periodic part of our IIC. The second contribution is unrelated to the IIC and justifies the common observation that the magnitude of finite-size dependence can strongly vary between vacancy and interstitial defects. It can be approximately predicted using atomic radius, but is strongly sensitive to the pseudopotential employed. Combined, these developments provide a new justification for known finite-size scaling rules. Our results suggest that for cubic supercells, the Lany-Zunger IIC, combined with simplified potential alignment between neutral systems, can yield accurate corrections in spite of the simplicity of the approach. Periodic boundary conditions ; Scaling ; Pseudopotential ; Physics ; Method of image charges ; Charge (physics) ; Supercell ; Dielectric ; Statistical physics ; Boundary (topology) ; Poisson's equation ; Image (mathematics) ; Boundary value problem ; Computational physics ; Quantum mechanics ; Mathematical analysis ; Mathematics ; Geometry ; Computer science Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Ferroelectric and Piezoelectric Materials
Authors: Gunnar Alexander Schmitz | Ove Christiansen
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation We study how with means of Gaussian Process Regression (GPR) geometry optimizations, which rely on numerical gradients, can be accelerated. The GPR interpolates a local potential energy surface on which the structure is optimized. It is found to be efficient to combine results on a low computational level (HF or MP2) with the GPR-calculated gradient of the difference between the low level method and the target method, which is a variant of explicitly correlated Coupled Cluster Singles and Doubles with perturbative Triples correction CCSD(F12*)(T) in this study. Overall convergence is achieved if both the potential and the geometry are converged. Compared to numerical gradient-based algorithms, the number of required single point calculations is reduced. Although introducing an error due to the interpolation, the optimized structures are sufficiently close to the minimum of the target level of theory meaning that the reference and predicted minimum only vary energetically in the μE<sub>h</sub> regime. Gaussian process ; Kriging ; Regression ; Process (computing) ; Computer science ; Regression analysis ; Gaussian ; Geometry ; Mathematics ; Algorithm ; Statistics ; Machine learning ; Physics ; Programming language Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced Multi-Objective Optimization Algorithms
Authors: Wenwen Kong | Chu Zhang | Wei‐Hao Huang | Fei Liu | Yong He
Source: Sensors (article, 2018-01-04)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems Hyperspectral imaging covering the spectral range of 384-1034 nm combined with chemometric methods was used to detect <i>Sclerotinia sclerotiorum</i> (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems. Hyperspectral imaging ; Linear discriminant analysis ; Pattern recognition (psychology) ; Sclerotinia sclerotiorum ; Calibration ; Artificial intelligence ; Partial least squares regression ; Mathematics ; Discriminant ; Set (abstract data type) ; Computer science ; Scanner ; Artificial neural network ; Statistics ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Phytochemicals and Antioxidant Activities
Authors: Simona Pili | Peter Rought | Daniil I. Kolokolov | Longfei Lin | Iván da Silva | Yongqiang Cheng | Christopher Marsh | Ian P. Silverwood | Victoria García Sakai | Ming Li | Jeremy J. Titman | Lyndsey Knight | Luke L. Daemen | Anibal J. Ramirez‐Cuesta | Chiu C. Tang | Alexander G. Stepanov | Sihai Yang⧫ | Martin Schröder
Source: Chemistry of Materials (article, 2018-09-24)
Field: Chemistry — Inorganic Chemistry

Enhancement of Proton Conductivity in Nonporous Metal–Organic Frameworks: The Role of Framework Proton Density and Humidity Owing to their inherent pore structure, porous metal–organic frameworks (MOFs) can undergo postsynthetic modification, such as loading extra-framework proton carriers. However, strategies for improving the proton conductivity for nonporous MOFs are largely lacking, although increasing numbers of nonporous MOFs exhibit promising proton conductivities. Often, high humidity is required for nonporous MOFs to achieve high conductivities, but to date no clear mechanisms have been experimentally identified. Here we describe the new materials MFM-550(M), [M(HL1)], (H4L1 = biphenyl-4,4′-diphosphonic acid; M = La, Ce, Nd, Sm, Gd, Ho), MFM-550(Ba), [Ba(H2L1)], and MFM-555(M), [M(HL2)], (H4L2 = benzene-1,4-diphosphonic acid; M = La, Ce, Nd, Sm, Gd, Ho), and report enhanced proton conductivities in these nonporous materials by (i) replacing the metal ion to one with a lower oxidation state, (ii) reducing the length of the organic ligand, and (iii) introducing additional acidic protons on the MOF surface. Increased framework proton density in these materials can lead to an enhancement in proton conductivity of up to 4 orders of magnitude. Additionally, we report a comprehensive investigation using in situ 2H NMR and neutron spectroscopy, coupled with molecular dynamic modeling, to elucidate the role of humidity in assembling interconnected networks for proton hopping. This study constructs a relationship between framework proton density and the corresponding proton conductivity in nonporous MOFs, and directly explains the role of both surface protons and external water in assembling the proton conduction pathways. Proton ; Porous medium ; Metal-organic framework ; Conductivity ; Porosity ; Proton transport ; Chemistry ; Materials science ; Nanotechnology ; Chemical physics ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Adsorption ; Nuclear physics Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing ; Crystallography and molecular interactions
Authors: Daniel L. Winter | Marc R. Wilkins | William A. Donald
Source: Trends in biotechnology (review, 2018-09-04)
Field: Chemistry — Spectroscopy

Differential Ion Mobility–Mass Spectrometry for Detailed Analysis of the Proteome Ion-mobility spectrometry ; Mass spectrometry ; Proteome ; Chemistry ; Differential (mechanical device) ; Ion ; Computational biology ; Chromatography ; Biology ; Biochemistry ; Physics ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Nan Li | Yanyan Liu | Yan Li | Jia Bao Zhuang | Rong Rong Cui | Qian Gong | Na Zhao | Ben Zhong Tang
Source: ACS Applied Materials & Interfaces (article, 2018-06-25)
Field: Materials Science — Materials Chemistry

Fine Tuning of Emission Behavior, Self-Assembly, Anion Sensing, and Mitochondria Targeting of Pyridinium-Functionalized Tetraphenylethene by Alkyl Chain Engineering Compared to the many studies that focus on the development of novel molecular frameworks pertaining to functionalized fluorescent materials, there is lesser emphasis on side chains even though they have a significant impact on the properties and applications of fluorescent materials. In this study, a series of pyridinium-functionalized tetraphenylethene salts (TPEPy-1 to TPEPy-4) possessing different alkyl chains are synthesized, and the influence of chain length on their optical performance and applications is thoroughly investigated. By changing the alkyl chain, the fluorogens exhibit opposite emission behavior in aqueous media because of their distinct hydrophobic nature, and their solid-state emission can be fine-tuned from green to red owing to their distinct molecular configuration. In addition, by increasing the chain length, the microstructure of the self-assembled fluorogens converts from microplates to microrods with various emission colors. Moreover, TPEPy-1 exhibits dual-mode fluorescence "turn-on" response toward NO<sub>3</sub><sup>-</sup> and ClO<sub>4</sub><sup>-</sup> in aqueous media because the anions induce the self-assembly of fluorogens. Furthermore, the fluorogens display cellular uptake selectivity while the proper alkyl chain impels the fluorogens to penetrate the cell membrane and accumulate in the mitochondria with high specificity. Pyridinium ; Materials science ; Alkyl ; Aggregation-induced emission ; Ion ; Chain (unit) ; Self-assembly ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry ; Chemical engineering ; Fluorescence ; Chemistry Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Polydiacetylene-based materials and applications
Authors: Weizong Xu | James M. LeBeau
Source: Ultramicroscopy (article, 2018-03-05)
Field: Materials Science — Surfaces, Coatings and Films

A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns Computer science ; Position (finance) ; Convolutional neural network ; Tilt (camera) ; Artificial neural network ; Diffraction ; Rotation (mathematics) ; Algorithm ; Deep learning ; Pattern recognition (psychology) ; Artificial intelligence ; Optics ; Physics ; Mathematics ; Geometry Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Liang Cao | Chenyang Li | Tim Mueller
Source: Journal of Chemical Information and Modeling (article, 2018-09-18)
Field: Materials Science — Materials Chemistry

The Use of Cluster Expansions To Predict the Structures and Properties of Surfaces and Nanostructured Materials The construction of cluster expansions parametrized by first-principles calculations is a powerful tool for calculating properties of materials. In this Perspective, we discuss the application of cluster expansions to surfaces and nanomaterials. We review the fundamentals of the cluster expansion formalism and how machine learning is used to improve the predictive accuracy of cluster expansions. We highlight several representative applications of cluster expansions to surfaces and nanomaterials, demonstrating how cluster expansions help researchers build structure-property relationships and enable rational design to accelerate the discovery of new materials. Potential applications and future challenges of cluster expansions are also discussed. Cluster (spacecraft) ; Formalism (music) ; Cluster expansion ; Computer science ; Perspective (graphical) ; Nanotechnology ; Artificial intelligence ; Materials science ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; nanoparticles nucleation surface interactions
Authors: Xiaomei Wang | Han Ou | Jianhan Huang
Source: Journal of Colloid and Interface Science (article, 2018-12-06)
Field: Materials Science — Materials Chemistry

One-pot synthesis of hyper-cross-linked polymers chemically modified with pyrrole, furan, and thiophene for phenol adsorption from aqueous solution Thiophene ; Adsorption ; Furan ; Heteroatom ; Chemistry ; Pyrrole ; Polymer ; Aqueous solution ; Phenol ; Polymer chemistry ; Organic chemistry ; Inorganic chemistry Covalent Organic Framework Applications ; Conducting polymers and applications ; Metal-Organic Frameworks: Synthesis and Applications