jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vı́ctor Cerdà | Piyawan Phansi | Sergio Ferreira
Source: TrAC Trends in Analytical Chemistry (review, 2022-09-21)
Field: Chemistry — Spectroscopy

From mono- to multicomponent methods in UV-VIS spectrophotometric and fluorimetric quantitative analysis – A review In this work, a review of different methods used for the simultaneous determination of several components through spectrophotometric and fluorimetric measurements is carried out including derivative methods. Several methods are described, like multilinear regression analysis and partial least squares, as well as some other for non-linear systems, like the Gauss-Newton, simplex and neural network methods. The effect of the contribution of unknown species has been considered. A novel approach related with the simultaneous determination of a catalyst and an activator or inhibitor has been included. Several manual and automated methods using flow techniques are described, including the resolution of overlapped HPLC peaks. Resolution of compound mixtures with highly overlapped spectra • Different methods for the simultaneous determination of several components are considered • Spectrophotometric and fluorimetric measurements, as well as their derivative methods are taken into account. • Multilinear regression analysis and partial least squares are described • Also some other for non-linear systems like the Gauss-Newton, simplex and neural network methods are described • A novel approach for the simultaneous determination of a catalyst and an activator has been included Multilinear map ; Partial least squares regression ; Artificial neural network ; Gauss ; Resolution (logic) ; Simplex algorithm ; Chemistry ; Biological system ; Computer science ; Analytical Chemistry (journal) ; Chromatography ; Artificial intelligence ; Mathematics ; Algorithm ; Linear programming ; Physics ; Machine learning Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Debjyoti Bhattacharya | Devon C. Kleeblatt | Antonia Statt | Wesley F. Reinhart
Source: Soft Matter (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structure. The dependence of this structure on the sequence necessarily implies that a mapping between the two exists, yet it has been difficult to model so far. Predicting the aggregation behavior of these macromolecules is challenging due to the lack of effective order parameters, a vast design space, inherent variability, and high computational costs associated with currently available simulation techniques. Here, we accurately predict the morphology of aggregates self-assembled from sequence-defined macromolecules using supervised machine learning. We find that regression models with implicit representation learning perform significantly better than those based on engineered features such as <i>k</i>-mer counting, and a recurrent-neural-network-based regressor performs the best out of nine model architectures we tested. Furthermore, we demonstrate the high-throughput screening of monomer sequences using the regression model to identify candidates for self-assembly into selected morphologies. Our strategy is shown to successfully identify multiple suitable sequences in every test we performed, so we hope the insights gained here can be extended to other increasingly complex design scenarios in the future, such as the design of sequences under polydispersity and at varying environmental conditions. Sequence (biology) ; Representation (politics) ; Computer science ; Aggregate (composite) ; Artificial neural network ; Macromolecule ; Artificial intelligence ; Biological system ; Sequence space ; Machine learning ; Algorithm ; Nanotechnology ; Chemistry ; Mathematics ; Materials science ; Biology Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Zedong Wang | Jiawei Dong | Jing Qiu | Linjun Wang
Source: ACS Applied Materials & Interfaces (review, 2022-01-31)
Field: Materials Science — Materials Chemistry

All-Atom Nonadiabatic Dynamics Simulation of Hybrid Graphene Nanoribbons Based on Wannier Analysis and Machine Learning Trajectory surface hopping combined with ab initio electronic structure calculations is a popular and powerful approach for on-the-fly nonadiabatic dynamics simulations. For large systems, however, this remains a significant challenge because of the unaffordable computational cost of large-scale electronic structure calculations. Here, we present an efficient divide-and-conquer approach to construct the system Hamiltonian based on Wannier analysis and machine learning. In detail, the large system under investigation is first decomposed into small building blocks, and then all possible segments formed by building blocks within a cutoff distance are found out. Ab initio molecular dynamics is carried out to generate a sequence of geometries for each equivalent segment with periodicity. The Hamiltonian matrices in the maximum localized Wannier function (MLWF) basis are obtained for all geometries and utilized to train artificial neural networks (ANNs) for the structure-dependent Hamiltonian elements. Taking advantage of the orthogonality and spatial locality of MLWFs, the one-electron Hamiltonian of a large system at arbitrary geometry can be directly constructed by the trained ANNs. As demonstrations, we study charge transport in a zigzag graphene nanoribbon (GNR), a coved GNR, and a series of hybrid GNRs with a state-of-the-art surface hopping method. The interplay between delocalized and localized states is found to determine the electron dynamics in hybrid GNRs. Our approach has successfully studied GNRs with >10 000 atoms, paving the way for efficient and reliable all-atom nonadiabatic dynamics simulation of general systems. Wannier function ; Hamiltonian (control theory) ; Surface hopping ; Delocalized electron ; Electronic structure ; Ab initio ; Zigzag ; Molecular dynamics ; Graphene ; Hamiltonian matrix ; Tight binding ; Wave function ; Physics ; Materials science ; Topology (electrical circuits) ; Statistical physics ; Quantum mechanics ; Eigenvalues and eigenvectors ; Mathematics ; Geometry Machine Learning in Materials Science ; Graphene research and applications ; Quantum and electron transport phenomena
Authors: Yasir Mahmood | Maher B. Alghalayini | Enrique Martínez | Christiaan J. J. Paredis | Fadi Abdeljawad
Source: Scientific Reports (article, 2022-04-23)
Field: Materials Science — Materials Chemistry

Atomistic and machine learning studies of solute segregation in metastable grain boundaries The interaction of alloying elements with grain boundaries (GBs) influences many phenomena, such as microstructural evolution and transport. While GB solute segregation has been the subject of active research in recent years, most studies focus on ground-state GB structures, i.e., lowest energy GBs. The impact of GB metastability on solute segregation remains poorly understood. Herein, we leverage atomistic simulations to generate metastable structures for a series of [001] and [110] symmetric tilt GBs in a model Al-Mg system and quantify Mg segregation to individual sites within these boundaries. Our results show large variations in the atomic Voronoi volume due to GB metastability, which are found to influence the segregation energy. The atomistic data are then used to train a Gaussian Process machine learning model, which provides a probabilistic description of the GB segregation energy in terms of the local atomic environment. In broad terms, our approach extends existing GB segregation models by accounting for variability due to GB metastability, where the segregation energy is treated as a distribution rather than a single-valued quantity. Metastability ; Grain boundary ; Leverage (statistics) ; Energy landscape ; Materials science ; Statistical physics ; Voronoi diagram ; Molecular dynamics ; Physics ; Computer science ; Chemistry ; Thermodynamics ; Artificial intelligence ; Microstructure ; Computational chemistry ; Mathematics ; Metallurgy Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties
Authors: Xiangyun Gao | Lei Bao | Yi Zhang | Daxu Zhang | Chong Wei | Laifei Cheng | Litong Zhang | Xuqin Li | Hao Ding
Source: Materials Characterization (article, 2022-12-21)
Field: Materials Science — Ceramics and Composites

Identification of microstructures and damages in silicon carbide ceramic matrix composites by deep learning Materials science ; Composite material ; Silicon carbide ; Chemical vapor infiltration ; Ceramic matrix composite ; Ceramic ; Microstructure ; Composite number ; Ultimate tensile strength ; Filament winding ; Porosity Advanced ceramic materials synthesis ; Advanced Surface Polishing Techniques ; Aluminum Alloys Composites Properties
Authors: Xinyi Wang | Wencheng Lin | Changming Chen | Liubing Kong | Zhuoru Huang | Dmitry Kirsanov | Andrey Legin | Hao Wan | Ping Wang
Source: Sensors and Actuators B Chemical (article, 2022-04-19)
Field: Chemistry — Electrochemistry

Neural networks based fluorescence and electrochemistry dual-modal sensor for sensitive and precise detection of cadmium and lead simultaneously Cadmium ; Sensitivity (control systems) ; Fluorescence ; Artificial neural network ; Flexibility (engineering) ; Biological system ; Modal ; Interference (communication) ; Electrochemistry ; Computer science ; Dual (grammatical number) ; Materials science ; Chemistry ; Environmental science ; Physics ; Artificial intelligence ; Electronic engineering ; Mathematics ; Engineering ; Statistics ; Telecommunications ; Optics Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Fujia Dong | Yongzhao Bi | Jie Hao | Sijia Liu | Yu Lv | Jiarui Cui | Songlei Wang | Yafang Han | Argenis Rodas‐González
Source: Biosensors (article, 2022-11-18)
Field: Chemistry — Analytical Chemistry

A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near-infrared hyperspectral imaging (NIR-HSI) combined with two-dimensional correlation spectroscopy (2D-COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D-COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH<sub>2</sub> in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136-1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D-COS analysis, and provided excellent results (R<sup>2</sup><sub>C</sub> of 0.8141, R<sup>2</sup><sub>P</sub> of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D-COS combined with NIR-HSI could be used as an effective method to monitor Ala content in beef. Hyperspectral imaging ; Alanine ; Near-infrared spectroscopy ; Infrared ; Chemistry ; Remote sensing ; Optics ; Physics ; Biochemistry ; Geology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Théophile Tchakoua | Nick Gerrits | Egidius W. F. Smeets | Geert–Jan Kroes
Source: Journal of Chemical Theory and Computation (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

SBH17: Benchmark Database of Barrier Heights for Dissociative Chemisorption on Transition Metal Surfaces Accurate barriers for rate controlling elementary reactions on metal surfaces are key to understanding, controlling, and predicting the rate of heterogeneously catalyzed processes. While barrier heights for gas phase reactions have been extensively benchmarked, dissociative chemisorption barriers for the reactions of molecules on metal surfaces have received much less attention. The first database called SBH10 and containing 10 entries was recently constructed based on the specific reaction parameter approach to density functional theory (SRP-DFT) and experimental results. We have now constructed a new and improved database (SBH17) containing 17 entries based on SRP-DFT and experiments. For this new SBH17 benchmark study, we have tested three algorithms (high, medium, and light) for calculating barrier heights for dissociative chemisorption on metals, which we have named for the amount of computational effort involved in their use. We test the performance of 14 density functionals at the GGA, GGA+vdW-DF, and meta-GGA rungs. Our results show that, in contrast with the previous SBH10 study where the BEEF-vdW-DF2 functional seemed to be most accurate, the workhorse functional PBE and the MS2 density functional are the most accurate of the GGA and meta-GGA functionals tested. Of the GGA+vdW functionals tested, the SRP32-vdW-DF1 functional is the most accurate. Additionally, we found that the medium algorithm is accurate enough for assessing the performance of the density functionals tested, while it avoids geometry optimizations of minimum barrier geometries for each density functional tested. The medium algorithm does require metal lattice constants and interlayer distances that are optimized separately for each functional. While these are avoided in the light algorithm, this algorithm is found not to give a reliable description of functional performance. The combination of relative ease of use and demonstrated reliability of the medium algorithm will likely pave the way for incorporation of the SBH17 database in larger databases used for testing new density functionals and electronic structure methods. Density functional theory ; Chemisorption ; Materials science ; Metal ; Benchmark (surveying) ; Transition metal ; Chemical physics ; Computer science ; Computational chemistry ; Chemistry ; Physical chemistry ; Adsorption ; Catalysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures
Authors: Shaohua Zhang | Wei Li | Jiabin Luan | Abhinav Srivastava | Vincenzo Carnevale | Michael L. Klein | Jiawei Sun | Danni Wang | Serena P. Teora | Sjoerd J. Rijpkema | Johannes D. Meeldijk | Daniela A. Wilson
Source: Nature Chemistry (article, 2022-11-21)
Field: Materials Science — Surfaces, Coatings and Films

Adaptive insertion of a hydrophobic anchor into a poly(ethylene glycol) host for programmable surface functionalization Covalent and non-covalent molecular binding are two strategies to tailor surface properties and functions. However, the lack of responsiveness and requirement for specific binding groups makes spatiotemporal control challenging. Here, we report the adaptive insertion of a hydrophobic anchor into a poly(ethylene glycol) (PEG) host as a non-covalent binding strategy for surface functionalization. By using polycyclic aromatic hydrocarbons as the hydrophobic anchor, hydrophilic charged and non-charged functional modules were spontaneously loaded onto PEG corona in 2 min without the assistance of any catalysts and binding groups. The thermodynamically favourable insertion of the hydrophobic anchor can be reversed by pulling the functional module, enabling programmable surface functionalization. We anticipate that the adaptive molecular recognition between the hydrophobic anchor and the PEG host will challenge the hydrophilic understanding of PEG and enhance the progress in nanomedicine, advanced materials and nanotechnology. Surface modification ; Chemistry ; Ethylene glycol ; Covalent bond ; PEG ratio ; Hydrophobic effect ; Combinatorial chemistry ; Nanomedicine ; Nanotechnology ; Polymer chemistry ; Nanoparticle ; Organic chemistry ; Materials science Polymer Surface Interaction Studies ; Supramolecular Self-Assembly in Materials ; Dendrimers and Hyperbranched Polymers
Authors: Wei Chen | Karolina A. Tarach | Xianfeng Yi | Zhiqiang Liu | Xiaomin Tang | Kinga Góra‐Marek | Anmin Zheng
Source: Nature Communications (article, 2022-11-19)
Field: Chemistry — Inorganic Chemistry

Charge-separation driven mechanism via acylium ion intermediate migration during catalytic carbonylation in mordenite zeolite By employing ab initio molecular dynamic simulations, solid-state NMR spectroscopy, and two-dimensional correlation analysis of rapid scan Fourier transform infrared spectroscopy data, a new pathway is proposed for the formation of methyl acetate (MA) via the acylium ion (i.e.,CH<sub>3</sub> - C ≡ O<sup>+</sup>) in 12-membered ring (MR) channel of mordenite by an integrated reaction/diffusion kinetics model, and this route is kinetically and thermodynamically more favorable than the traditional viewpoint in 8MR channel. From perspective of the complete catalytic cycle, the separation of these two reaction zones, i.e., the C-C bond coupling in 8MR channel and MA formation in 12MR channel, effectively avoids aggregation of highly active acetyl species or ketene, thereby reducing undesired carbon deposit production. The synergistic effect of different channels appears to account for the high carbonylation activity in mordenite that has thus far not been fully explained, and this paradigm may rationalize the observed catalytic activity of other reactions. Mordenite ; Ketene ; Catalysis ; Chemistry ; Computational chemistry ; Zeolite ; Photochemistry ; Chemical physics ; Organic chemistry Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications ; Advanced Battery Materials and Technologies
Authors: Sven Roediger | Sebastian U. Leutenegger | Bill Morandi
Source: Chemical Science (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Nickel-catalysed diversification of phosphine ligands by formal substitution at phosphorus Phosphine substituents can be exchanged by standard alkylation of a phosphine and a subsequent dearylation of the resulting phosphonium salt. A wide variety of alkyl groups can be introduced into both mono- and bidentate ligands using this method. Phosphine ; Chemistry ; Alkylation ; Adduct ; Alkyl ; Borane ; Substituent ; Phosphonium ; Nickel ; Ligand (biochemistry) ; Combinatorial chemistry ; Organic chemistry ; Medicinal chemistry ; Catalysis Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry ; Chemical Synthesis and Analysis
Authors: Saveer Ahmad Khandy | Dinesh C. Gupta
Source: Scientific Reports (article, 2022-11-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DFT analogue of prospecting the spin-polarised properties of layered perovskites Ba2ErNbO6 and Ba2TmNbO6 influenced by electronic structure Since the unexpected accelerated discovery of half-metallic perovskites is continuously on the rise both from basic sciences and application-oriented sides. Herein, for the first time in this carried research work, we significantly delivered a detailed analysis on one of experimentally synthesized perovskite structure Ba<sub>2</sub>ErNbO<sub>6</sub> and in related to Ba<sub>2</sub>TmNbO<sub>6</sub> within the realm of unified density functional theory. Initially, the structural stability of two molecular perovskite structures were critically established interms of their total ground state and cohesive energies by the expendition of Brich Murnaghan equation of state. Also, the tolerance factor (τ) oversees the cubic structural stability without possessing any geometrical strains. More likely, the density functional perturbation theory (DFPT) has been calibrated to perceive the dynamical context of these layered structures. Also, from the understandings of second order elastic and mechanical parameters adresses their suitable ductile characteristics. The quantum mechanical refinement of their intrinsic electronic structures were systematically tuned by the exploitation of Generalised gradient approximation (GGA), on-site Hubbard scheme (GGA + U) selected to the strongly correlated electrons of particular angular momentum and modified Becke-Johnson (mBJ) potential. Moreover, the two-dimensional representation of asymmetric density of states (DOS) pinned around the Fermi-level (E<sub>F</sub>) and the interpretation linked to their corresponding spin-polarised band structures signatures the well-known half-metallic nature. Subsequently, the transport properties especially the value of figure of merit (<sub>Z</sub>T) equals to unity (1) along the selected chemical potential range at different temperatures. The summed-up properties and the overall tendency triggers the possibility of these materials to register their extending applications in spintronics, thermoelectrics, nanoengineering, and radioisotope generator perspectives. Density functional theory ; Condensed matter physics ; Perovskite (structure) ; Physics ; Ground state ; Electron ; Electronic structure ; Structural stability ; Perturbation theory (quantum mechanics) ; Fermi surface ; Context (archaeology) ; Materials science ; Quantum mechanics ; Chemistry ; Geology ; Crystallography Magnetic and transport properties of perovskites and related materials ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Daniele Coslovich | Robert L. Jack | Joris Paret
Source: The Journal of Chemical Physics (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

Dimensionality reduction of local structure in glassy binary mixtures We consider unsupervised learning methods for characterizing the disordered microscopic structure of supercooled liquids and glasses. Specifically, we perform dimensionality reduction of smooth structural descriptors that describe radial and bond-orientational correlations and assess the ability of the method to grasp the essential structural features of glassy binary mixtures. In several cases, a few collective variables account for the bulk of the structural fluctuations within the first coordination shell and also display a clear connection with the fluctuations of particle mobility. Fine-grained descriptors that characterize the radial dependence of bond-orientational order better capture the structural fluctuations relevant for particle mobility but are also more difficult to parameterize and to interpret. We also find that principal component analysis of bond-orientational order parameters provides identical results to neural network autoencoders while having the advantage of being easily interpretable. Overall, our results indicate that glassy binary mixtures have a broad spectrum of structural features. In the temperature range we investigate, some mixtures display well-defined locally favored structures, which are reflected in bimodal distributions of the structural variables identified by dimensionality reduction. Curse of dimensionality ; Dimensionality reduction ; Binary number ; Principal component analysis ; Statistical physics ; Particle (ecology) ; Materials science ; Biological system ; Chemical physics ; Computer science ; Artificial intelligence ; Physics ; Mathematics Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase-change materials and chalcogenides