jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ben Zhang | Zhijie Cheng | Yingying Wu | Lei Chen | Rong Jing | Xingwei Cai | Chunhui Jiang | Yi‐Quan Zhang | Aihua Yuan | Hui−Hui Cui | Zhao‐Yang Li
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Pseudo-mono-axial ligand fields that support high energy barriers in triangular dodecahedral Dy(<scp>iii</scp>) single-ion magnets The synthesis of air-stable, high-performance single-molecule magnets (SMMs) is of great significance for their practical applications. Indeed, Ln complexes with high coordination numbers are satisfactorily air stable. However, such geometries easily produce spherical ligand fields that minimize magnetic anisotropy. Herein, we report the preparation of three air-stable eight-coordinate mononuclear Dy(iii) complexes with triangular dodecahedral geometries, namely, [Dy(BPA-TPA)Cl](BPh<sub>4</sub>)<sub>2</sub> (1) and [Dy(BPA-TPA)(X)](BPh<sub>4</sub>)<sub>2</sub>·<i>n</i>CH<sub>2</sub>Cl<sub>2</sub> (X = CH<sub>3</sub>O<sup>-</sup> and <i>n</i> = 1 for 2; L = PhO<sup>-</sup> and <i>n</i> = 2 for 3), using a novel design concept in which the bulky heptadentate [2,6-bis[bis(2-pyridylmethyl)amino]methyl]-pyridine (BPA-TPA) ligand enwraps the Dy(iii) ion through weak coordinate bonds leaving only a small vacancy for a negatively charged (Cl<sup>-</sup>), methoxy (CH<sub>3</sub>O<sup>-</sup>) or phenoxy (PhO<sup>-</sup>) moiety to occupy. Magnetic measurements reveal that the single-molecule magnet (SMM) property of complex 1 is actually poor, as there is almost no energy barrier. However, complexes 2 and 3 exhibit fascinating SMM behavior with high energy barriers (<i>U</i> <sub>eff</sub> = 686 K for 2; 469 K for 3) and magnetic hysteresis temperatures up to 8 K, which is attributed to the pseudolinear ligand field generated by one strong, highly electrostatic Dy-O bond. <i>Ab initio</i> calculations were used to show the apparent difference in the magnetic dynamics of the three complexes, confirming that the pseudo-mono-axial ligand field has an important effect on high-performance SMMs compared with the local symmetry. This study not only presents the highest energy barrier for a triangular dodecahedral SMM but also highlights the enormous potential of the pseudolinear Dy-L ligand field for constructing promising SMMs. Dodecahedron ; Magnet ; Ion ; Ligand (biochemistry) ; Lanthanide ; Materials science ; Crystallography ; Chemistry ; Engineering ; Mechanical engineering ; Receptor ; Organic chemistry Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Advanced NMR Techniques and Applications
Authors: Zaidatul Hanis Azmi | Siti Nurnadiah Mohd Aris | Shamsu Abubakar | Suresh Sagadevan | Rikson Siburian | Suriati Paiman
Source: Coatings (article, 2022-03-31)
Field: Materials Science — Materials Chemistry

Effect of Seed Layer on the Growth of Zinc Oxide Nanowires by Chemical Bath Deposition Method This study concentrated on the effect of the ZnO seed layer on the growth of ZnO nanowires by the chemical bath deposition method. Multilayer coatings were used to control the thickness of the seed layer of one layer, three layers, and five layers. The seed layer thickness was measured using a profilometer at 14.43, 33.31, and 53.13 nm for one-layer, three-layer, and five-layer samples, respectively. The samples were then immersed in a chemical bath deposition solution at 90 °C for 3 h to allow ZnO nanowires to grow. The X-ray diffraction (XRD) pattern of seed layers revealed a polycrystalline wurtzite structure with (101) orientation as the dominant peak in all samples. Field emission scanning electron microscopy (FESEM) revealed that ZnO nanowires grown in a single layer have a flower-like structure, whereas ZnO nanowires grown in three and five layers have a rod-like structure. Further, ImageJ software revealed that the diameter and length of the ZnO nanowires were in the 40–90 nm and 100–900 nm ranges, respectively. The five-layer sample had the highest density of ZnO nanowires at 668 µm−2, followed by the three-layer sample and the one-layer sample. However, the ZnO nanowires in the five-layer and one-layer samples are not vertically aligned. On the other hand, the three-layer sample had the best vertical alignment of this group of samples. Nanowire ; Wurtzite crystal structure ; Layer (electronics) ; Materials science ; Crystallite ; Chemical bath deposition ; Scanning electron microscope ; Deposition (geology) ; Layer by layer ; Nanotechnology ; Zinc ; Composite material ; Metallurgy ZnO doping and properties ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Authors: S. A. Cotton | Paul R. Raithby | Alexander Shield | Jack M. Harrowfield
Source: Coordination Chemistry Reviews (article, 2022-01-03)
Field: Chemistry — Inorganic Chemistry

A comparison of the structural chemistry of scandium, yttrium, lanthanum and lutetium: A contribution to the group 3 debate Lutetium ; Scandium ; Yttrium ; Lanthanum ; Chemistry ; Coordination number ; Lanthanide ; Coordination complex ; Metal ; Inorganic chemistry ; Ion ; Crystallography ; Organic chemistry Radioactive element chemistry and processing ; Lanthanide and Transition Metal Complexes ; Inorganic Chemistry and Materials
Authors: Pedro M. Silva | Cristina Prieto | Cristiane Conte Paim de Andrade | José M. Lagarón | Lorenzo Pastrana | Manuel A. Coimbra | António A. Vicente | Miguel A. Cerqueira
Source: International Journal of Biological Macromolecules (article, 2022-01-11)
Field: Materials Science — Biomaterials

Hydroxypropyl methylcellulose-based micro- and nanostructures for encapsulation of melanoidins: Effect of electrohydrodynamic processing variables on morphological and physicochemical properties Electrohydrodynamic processing (EHDP) allows the use of a wide range of biopolymers and solvents, including food-grade biopolymers and green solvents, for the development of micro- and nanostructures. These structures present a high surface-area-to-volume ratio and different shapes and morphologies. The aim of this work was to design and produce hydroxypropyl methylcellulose (HPMC)-based micro- and nanostructures through EHD processing using green solvents, while exploring the influence of process and solution parameters, and incorporating a bioactive extracted from a food by-product. Low (LMW) and high (HMW) molecular weight HPMC have been used as polymers. The design-of-experiments methodology was used to determine the effects of process parameters (polymer concentration, flow rate, tip-to-collector distance, and voltage) of EHDP on the particle and fibre diameter, aspect ratio, diameter distribution, aspect ratio distribution, and percentage of fibre breakage. Additionally, melanoidins extracted from spent coffee grounds were encapsulated into the HPCM-based structures at a concentration of 2.5 mg melanoidins/mL of the polymer solution. Polymer solutions were characterised regarding their viscosity, surface tension and conductivity, and showed that the incorporation of melanoidins increased the viscosity and conductivity values of the polymer solutions. The developed structures were characterised regarding their thermal properties, crystallinity and morphology before and after melanoidin incorporation and it was observed that melanoidin incorporation did not significantly influence the characteristics of the produced micro- and nanostructures. Based on the results, it is possible to envision the use of the produced micro- and nanostructures in a wide range of applications, both in food and biomedical fields. Polymer ; Electrohydrodynamics ; Chemical engineering ; Crystallinity ; Nanostructure ; Dispersity ; Materials science ; Chemistry ; Nanotechnology ; Polymer chemistry ; Composite material Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Nanomaterials and Printing Technologies
Authors: Joan Planas-Iglesias | Filip Opálený | Pavol Ulbrich | Jan Štourač | Zainab K. Sanusi | Gaspar Pinto | Andrea Schenkmayerová | Jan Byška | Jiřı́ Damborský | Barbora Kozlíková | David Bednář
Source: Nucleic Acids Research (article, 2022-04-14)
Field: Materials Science — Materials Chemistry

LoopGrafter: a web tool for transplanting dynamical loops for protein engineering The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses. Context (archaeology) ; Biology ; Protein engineering ; Web server ; Computational biology ; Transplantation ; Loop (graph theory) ; Process (computing) ; Computer science ; Function (biology) ; Graphical user interface ; Biological system ; Cell biology ; Enzyme ; Biochemistry ; Operating system ; Mathematics Enzyme Structure and Function ; Microfluidic and Capillary Electrophoresis Applications ; Glycosylation and Glycoproteins Research
Authors: Hsiao‐Yi Chen | Davide Sangalli | Marco Bernardi
Source: Physical Review Research (article, 2022-12-21)
Field: Materials Science — Materials Chemistry

First-principles ultrafast exciton dynamics and time-domain spectroscopies: Dark-exciton mediated valley depolarization in monolayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>WSe</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> Calculations combining first-principles electron-phonon ($e$-ph) interactions with the Boltzmann equation enable studies of ultrafast carrier and phonon dynamics. However, in materials with weak Coulomb screening, electrons and holes form bound excitons so their scattering processes become correlated, posing additional challenges for modeling nonequilibrium physics. Here we show calculations of ultrafast exciton dynamics and related time-domain spectroscopies using ab initio exciton-phonon (ex-ph) interactions together with an excitonic Boltzmann equation. Starting from the nonequilibrium exciton populations, we develop simulations of time-domain absorption and photoemission spectra that take into account electron-hole correlations. We use this method to study monolayer ${\mathrm{WSe}}_{2}$, where our calculations predict subpicosecond timescales for exciton relaxation and valley depolarization and reveal the key role of intermediate dark excitons. The approach introduced in this paper enables a quantitative description of nonequilibrium dynamics and ultrafast spectroscopies in materials with strongly bound excitons. Exciton ; Physics ; Relaxation (psychology) ; Phonon ; Condensed matter physics ; Boltzmann equation ; Scattering ; Electron ; Ultrashort pulse ; Heterojunction ; Quantum mechanics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Mujeeb Ahmad | Khushboo Agarwal | Sergio Gonzalez Munoz | Abhishek Ghosh | Nisha Kodan | Oleg Kolosov | B. R. Mehta
Source: Advanced Functional Materials (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Engineering Interfacial Effects in Electron and Phonon Transport of Sb<sub>2</sub>Te<sub>3</sub>/MoS<sub>2</sub> Multilayer for Thermoelectric <i>ZT</i> Above 2.0 Abstract Efficient thermoelectric (TE) conversion of waste heat to usable energy is a holy grail promising to address major societal issues related to energy crisis and global heat management. For these to be economical, synthesis of a solid‐state material with a high figure‐of‐merit ( ZT ) values is the key, with characterization methods quantifying TE and heat transport properties being indispensable for guiding the development of such materials. In the present study, a large enhancement of the TE power factor in Sb 2 Te 3 /MoS 2 multilayer structures is reported. A new approach is used to simultaneously experimentally determine the values of in‐plane ( k xy ) and out‐of‐pane ( k z ) thermal conductivities for multilayer samples with characteristic layer thickness of few nanometres, essential for the quantification of the ZT , the key parameter for the TE material. Combining simultaneous enhancement in the value of in‐plane power factor (to (4.9 ± 0.4) × mWm −1 K −2 ) and reduction of the in‐plane value of the thermal conductivity (to 0.7 ± 0.1 Wm −1 K −1 ) for Sb 2 Te 3 /MoS 2 multilayer sample led to high values of ZT of 2.08 ± 0.37 at room temperature. The present study, therefore, sets the foundation for a new methodology of exploiting the properties of 2D/3D interfaces for the development of novel fully viable thermoelectric materials. Materials science ; Thermoelectric effect ; Thermal conductivity ; Thermoelectric materials ; Figure of merit ; Phonon ; Engineering physics ; Energy conversion efficiency ; Optoelectronics ; Nanotechnology ; Condensed matter physics ; Thermodynamics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Abdulilah Mohammad Mayet | Seyed Mehdi Alizadeh | Karina Shamilyevna Nurgalieva | Robert Hanus | Ehsan Nazemi | Igor M. Narozhnyy
Source: Energies (article, 2022-03-09)
Field: Chemistry — Analytical Chemistry

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE &lt; 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry. Artificial neural network ; Multilayer perceptron ; Volume (thermodynamics) ; Petroleum ; Petrochemical ; Gasoline ; Correlation coefficient ; Sizing ; Monte Carlo method ; Detector ; Computer science ; Engineering ; Process engineering ; Petroleum engineering ; Artificial intelligence ; Chemistry ; Mathematics ; Statistics ; Machine learning ; Waste management ; Electrical engineering Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Chemical Sensor Technologies
Authors: Yuta Suzuki | Tatsunori Taniai | Kotaro Saito | Yoshitaka Ushiku | Kanta Ono
Source: Machine Learning Science and Technology (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Self-supervised learning of materials concepts from crystal structures via deep neural networks Abstract Material development involves laborious processes to explore the vast materials space. The key to accelerating these processes is understanding the structure-functionality relationships of materials. Machine learning has enabled large-scale analysis of underlying relationships between materials via their vector representations, or embeddings. However, the learning of material embeddings spanning most known inorganic materials has remained largely unexplored due to the expert knowledge and efforts required to annotate large-scale materials data. Here we show that our self-supervised deep learning approach can successfully learn material embeddings from crystal structures of over 120 000 materials, without any annotations, to capture the structure-functionality relationships among materials. These embeddings revealed the profound similarity between materials, or ‘materials concepts’, such as cuprate superconductors and lithium-ion battery materials from the unannotated structural data. Consequently, our results enable us to both draw a large-scale map of the materials space, capturing various materials concepts, and measure the functionality-aware similarities between materials. Our findings will enable more strategic approaches to material development. Scale (ratio) ; Computer science ; Similarity (geometry) ; Space (punctuation) ; Artificial intelligence ; Chemical space ; Deep learning ; Key (lock) ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Fabio Broccatelli | Richard Trager | Michael Reutlinger | George Karypis | Mufei Li
Source: Molecular Informatics (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces In this work, we benchmark a variety of single- and multi-task graph neural network (GNN) models against lower-bar and higher-bar traditional machine learning approaches employing human engineered molecular features. We consider four GNN variants - Graph Convolutional Network (GCN), Graph Attention Network (GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint (AttentiveFP). So far deep learning models have been primarily benchmarked using lower-bar traditional models solely based on fingerprints, while more realistic benchmarks employing fingerprints, whole-molecule descriptors and predictions from other related endpoints (e. g., LogD7.4) appear to be scarce for industrial ADME datasets. In addition to time-split test sets based on Genentech data, this study benefits from the availability of measurements from an external chemical space (Roche data). We identify GAT as a promising approach to implementing deep learning models. While all the deep learning models significantly outperform lower-bar benchmark traditional models solely based on fingerprints, only GATs seem to offer a small but consistent improvement over higher-bar benchmark traditional models. Finally, the accuracy of in vitro assays from different laboratories predicting the same experimental endpoints appears to be comparable with the accuracy of GAT single-task models, suggesting that most of the observed error from the models is a function of the experimental error propagation. Benchmark (surveying) ; Computer science ; ADME ; Artificial intelligence ; Machine learning ; Benchmarking ; Generalizability theory ; Artificial neural network ; Deep learning ; Graph ; Convolutional neural network ; Data mining ; Cheminformatics ; Bioinformatics ; Theoretical computer science ; Mathematics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kwangnam Kim | Aniruddha Dive | Andrew Grieder | Nicole Adelstein | ShinYoung Kang | Liwen F. Wan | Brandon C. Wood
Source: The Journal of Chemical Physics (article, 2022-05-16)
Field: Materials Science — Materials Chemistry

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li7La3Zr2O12 solid electrolytes Batteries based on solid-state electrolytes, including Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. These examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods. Grain boundary ; Ab initio ; Thermal diffusivity ; Materials science ; Interatomic potential ; Quantum ; Phase transition ; Molecular dynamics ; Statistical physics ; Computer science ; Chemical physics ; Condensed matter physics ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics ; Microstructure Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies