jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhengjun Qiu | Jian Chen | Yiying Zhao | Susu Zhu | Yong He | Chu Zhang
Source: Applied Sciences (article, 2018-01-31)
Field: Chemistry — Analytical Chemistry

Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network The feasibility of using hyperspectral imaging with convolutional neural network (CNN) to identify rice seed varieties was studied. Hyperspectral images of 4 rice seed varieties at two different spectral ranges (380–1030 nm and 874–1734 nm) were acquired. The spectral data at the ranges of 441–948 nm (Spectral range 1) and 975–1646 nm (Spectral range 2) were extracted. K nearest neighbors (KNN), support vector machine (SVM) and CNN models were built using different number of training samples (100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500 and 3000). KNN, SVM and CNN models in the Spectral range 2 performed slightly better than those in the Spectral range 1. The model performances improved with the increase in the number of training samples. The improvements were not significant when the number of training samples was large. CNN model performed better than the corresponding KNN and SVM models in most cases, which indicated the effectiveness of using CNN to analyze spectral data. The results of this study showed that CNN could be adopted in spectral data analysis with promising results. More varieties of rice need to be studied in future research to extend the use of CNNs in spectral data analysis. Hyperspectral imaging ; Convolutional neural network ; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Range (aeronautics) ; Spectral signature ; Spectral analysis ; Mathematics ; Remote sensing ; Physics ; Geography ; Engineering ; Spectroscopy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zhao Gao | Yifei Han | Feng Wang
Source: Nature Communications (article, 2018-09-24)
Field: Materials Science — Materials Chemistry

Cooperative supramolecular polymers with anthracene‒endoperoxide photo-switching for fluorescent anti-counterfeiting Innovative technologies are highly pursued for the detection and avoidance of counterfeiting in modern information society. Herein, we report the construction of photo-responsive supramolecular polymers toward fluorescent anti-counterfeit applications, by taking advantage of multicycle anthracene‒endoperoxide switching properties. Due to σ-metalation effect, photo-oxygenation of anthracene to endoperoxide is proceeded under the mild visible light irradiation conditions, while the backward conversion occurs spontaneously at room temperature. Supramolecular polymers are formed with cooperative nucleation‒elongation mechanism, which facilitate fluorescence resonance energy transfer process via two-component co-assembly strategy. Fluorescence resonance energy transfer efficiency is delicately regulated by either light-triggered anthracene‒endoperoxide conversion or vapor-induced monomer-polymer transition, leading to high-contrast fluorescent changes among three different states. On this basis, dual-mode anti-counterfeiting patterns have been successfully fabricated via inkjet printing techniques. Hence, cooperative supramolecular polymerization of photo-fluorochromic molecules represents an efficient approach toward high-performance anti-counterfeit materials with enhanced security reliability, fast response, and ease of operation. Anthracene ; Supramolecular chemistry ; Photochemistry ; Fluorescence ; Förster resonance energy transfer ; Supramolecular polymers ; Polymer ; Monomer ; Polymerization ; Nanotechnology ; Materials science ; Chemistry ; Molecule ; Organic chemistry Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications ; Supramolecular Self-Assembly in Materials
Authors: Prasanna V. Balachandran | Benjamin A. Kowalski | Alp Sehirlioglu | Turab Lookman
Source: Nature Communications (article, 2018-04-20)
Field: Materials Science — Materials Chemistry

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning Experimental search for high-temperature ferroelectric perovskites is a challenging task due to the vast chemical space and lack of predictive guidelines. Here, we demonstrate a two-step machine learning approach to guide experiments in search of xBi[Formula: see text]O<sub>3</sub>-(1 - x)PbTiO<sub>3</sub>-based perovskites with high ferroelectric Curie temperature. These involve classification learning to screen for compositions in the perovskite structures, and regression coupled to active learning to identify promising perovskites for synthesis and feedback. The problem is challenging because the search space is vast, spanning ~61,500 compositions and only 167 are experimentally studied. Furthermore, not every composition can be synthesized in the perovskite phase. In this work, we predict x, y, Me', and Me″ such that the resulting compositions have both high Curie temperature and form in the perovskite structure. Outcomes from both successful and failed experiments then iteratively refine the machine learning models via an active learning loop. Our approach finds six perovskites out of ten compositions synthesized, including three previously unexplored {Me'Me″} pairs, with 0.2Bi(Fe<sub>0.12</sub>Co<sub>0.88</sub>)O<sub>3</sub>-0.8PbTiO<sub>3</sub> showing the highest measured Curie temperature of 898 K among them. Materials science ; Ferroelectricity ; Algorithm ; Machine learning ; Perovskite (structure) ; Artificial intelligence ; Computer science ; Crystallography ; Chemistry ; Dielectric Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Topic Modeling
Authors: Benhui Hu | Cally Owh | Pei Lin Chee | Wan Ru Leow | Xuan Liu | Yun‐Long Wu | Peizhi Guo | Xian Jun Loh | Xiaodong Chen
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Biomaterials

Supramolecular hydrogels for antimicrobial therapy The emergence of drug-resistant microbes has become a threat to global health, and microbial infections severely limit the use of healthcare materials. To achieve efficient antimicrobial therapy, supramolecular hydrogels demonstrate unprecedented advantages in medical applications due to the tunable and reversible nature of their supramolecular interactions and the capability of hydrogels to incorporate various therapeutic agents. Herein, antimicrobial hydrogels are categorized according to their inherent antimicrobial properties or based on their roles in encapsulating antimicrobial materials. Moreover, strategies to further enhance the antimicrobial efficacy of hydrogels are highlighted, such as the incorporation of antifouling agents or the enabling of response towards physiological cues. We envision that supramolecular hydrogels, in combination with modern medical technology and devices, will contribute to the development of efficient and safe systems for antimicrobial therapy. Self-healing hydrogels ; Supramolecular chemistry ; Antimicrobial ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science ; Polymer chemistry ; Organic chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Supramolecular Chemistry and Complexes
Authors: Ouyang, R. | Curtarolo, S. | Ahmetcik, E. | Scheffler, M. | Ghiringhelli, L.
Source: MPG.PuRe (Max Planck Society) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates The lack of reliable methods for identifying descriptors - the sets of parameters capturing the underlying mechanisms of a materials property - is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials properties, within the framework of compressed-sensing based dimensionality reduction. SISSO (sure independence screening and sparsifying operator) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability are tested beyond the training data: It rediscovers the available pressure-induced insulator->metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development. Curse of dimensionality ; Computer science ; Binary number ; Property (philosophy) ; Materials science ; Artificial intelligence ; Data mining ; Dimensionality reduction ; Insulator (electricity) ; Machine learning ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Liming Wang | Zimeng Zhang | Yuchen Liu | Biran Wang | Lei Fang | Jingjing Qiu | Kun Zhang | Shiren Wang
Source: Nature Communications (article, 2018-09-13)
Field: Materials Science — Materials Chemistry

Exceptional thermoelectric properties of flexible organic−inorganic hybrids with monodispersed and periodic nanophase Flexible organic-inorganic hybrids are promising thermoelectric materials to recycle waste heat in versatile formats. However, current organic/inorganic hybrids suffer from inferior thermoelectric properties due to aggregate nanostructures. Here we demonstrate flexible organic-inorganic hybrids where size-tunable Bi<sub>2</sub>Te<sub>3</sub> nanoparticles are discontinuously monodispersed in the continuous conductive polymer phase, completely distinct from traditional bi-continuous hybrids. Periodic nanofillers significantly scatter phonons while continuous conducting polymer phase provides favored electronic transport, resulting in ultrahigh power factor of ~1350 μW m<sup>-1</sup> K<sup>-2</sup> and ultralow in-plane thermal conductivity of ~0.7 W m<sup>-1</sup> K<sup>-1</sup>. Consequently, figure-of-merit (ZT) of 0.58 is obtained at room temperature, outperforming all reported organic materials and organic-inorganic hybrids. Thermoelectric properties of as-fabricated hybrids show negligible change for bending 100 cycles, indicating superior mechanical flexibility. These findings provide significant scientific foundation for shaping flexible thermoelectric functionality via synergistic integration of organic and inorganic components. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Nanotechnology ; Phase (matter) ; Polymer ; Thermal conductivity ; Nanostructure ; Composite material ; Chemistry ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Chris S. G. Seo | Robert H. Morris
Source: Organometallics (article, 2018-12-20)
Field: Chemistry — Inorganic Chemistry

Catalytic Homogeneous Asymmetric Hydrogenation: Successes and Opportunities This is an overview of successes in the realm of catalytic homogeneous asymmetric hydrogenation of substrates primarily of interest in the synthesis of pharmaceuticals in order to identify important problems still unsolved. First, tables are provided that list the successful reductions to over 90% enantiomeric excess of prochiral ketones to alcohols, imines to amines, and olefins to saturated carbon centers. Noted in the tables are the metal (including “green” metals Mn, Fe, and Co) or enzyme, the class of ligand, the conditions of the medium, and the scale of reduction, if over 1 kg of product, as well as the nature of the process, whether direct hydrogenation using H2 gas (DH), transfer hydrogenation (TH), or hydrogenation with dynamic kinetic resolution (DKR). Tables of representative pharmaceutical or fine chemicals products are provided for each class of substrate. With this overview, the opportunities for further research and development become clearer. Chemistry ; Catalysis ; Homogeneous ; Asymmetric hydrogenation ; Homogeneous catalysis ; Catalytic hydrogenation ; Organic chemistry ; Combinatorial chemistry ; Enantioselective synthesis Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis ; Catalysis for Biomass Conversion
Authors: Chunhong Liang | Xiaodan Zhang | Ping Feng | Hongxiang Chai | Yuming Huang
Source: Chemical Engineering Journal (article, 2018-03-16)
Field: Chemistry — Organic Chemistry

ZIF-67 derived hollow cobalt sulfide as superior adsorbent for effective adsorption removal of ciprofloxacin antibiotics Adsorption ; Langmuir adsorption model ; Chemical engineering ; Aqueous solution ; Humic acid ; Materials science ; Physisorption ; Chemistry ; Ionic strength ; Inorganic chemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Adsorption and biosorption for pollutant removal
Authors: Jin‐Jin Li | Yin‐Ning Zhou | Zheng‐Hong Luo
Source: Progress in Polymer Science (review, 2018-07-21)
Field: Materials Science — Surfaces, Coatings and Films

Polymeric materials with switchable superwettability for controllable oil/water separation: A comprehensive review Nanotechnology ; Polymer ; Smart polymer ; Materials science ; Fabrication ; Smart material ; Process engineering ; Environmental science ; Computer science ; Engineering ; Composite material Surface Modification and Superhydrophobicity ; Electrohydrodynamics and Fluid Dynamics ; Fluid Dynamics and Heat Transfer
Authors: Riki J. Drout | Lee Robison | Omar K. Farha
Source: Coordination Chemistry Reviews (article, 2018-12-11)
Field: Chemistry — Inorganic Chemistry

Catalytic applications of enzymes encapsulated in metal–organic frameworks Chemistry ; Catalysis ; Reusability ; Metal-organic framework ; Enzyme ; Enzyme catalysis ; Thermal stability ; Nanotechnology ; Immobilized enzyme ; Biocatalysis ; Catalytic efficiency ; Macromolecule ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Biochemistry ; Reaction mechanism Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors
Authors: Sunae So | Junsuk Rho
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing nanophotonic structures using conditional deep convolutional generative adversarial networks Abstract Data‐driven design approaches based on deep learning have been introduced in nanophotonics to reduce time‐consuming iterative simulations, which have been a major challenge. Here, we report the first use of conditional deep convolutional generative adversarial networks to design nanophotonic antennae that are not constrained to predefined shapes. For given input reflection spectra, the network generates desirable designs in the form of images; this allows suggestions of new structures that cannot be represented by structural parameters. Simulation results obtained from the generated designs agree well with the input reflection spectrum. This method opens new avenues toward the development of nanophotonics by providing a fast and convenient approach to the design of complex nanophotonic structures that have desired optical properties. Nanophotonics ; Computer science ; Deep learning ; Reflection (computer programming) ; Artificial intelligence ; Generative grammar ; Convolutional neural network ; Nanotechnology ; Materials science Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Orbital Angular Momentum in Optics
Authors: Ashwini Nangia | Gautam R. Desiraju
Source: Angewandte Chemie International Edition (article, 2018-11-29)
Field: Materials Science — Materials Chemistry

Crystal Engineering: An Outlook for the Future Crystal Engineering has traditionally dealt with molecular crystals. It is the understanding of intermolecular interactions in the context of crystal packing and in the utilization of such understanding in the design of new solids with desired physical and chemical properties. We outline here five areas which come under the umbrella of Crystal Engineering and where we feel that a proper planning of research efforts could lead to higher dividends for science together with greater returns for humankind. We touch on themes and domains where science funding and translation efforts could be directed in the current climate of a society that increasingly expects applications and utility products from science and technology. The five topics are: 1) pharmaceutical solids; 2) industrial solid state reactions; 3) mechanical properties with practical applications; 4) MOFs and COFs framework solids; 5) new materials for solar energy harvesting and advanced polymers. Engineering ethics ; Engineering ; Political science ; Materials science Crystallization and Solubility Studies ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Federico M. Paruzzo | Albert Hofstetter | Félix Musil | Sandip De | Michele Ceriotti | Lyndon Emsley
Source: Nature Communications (article, 2018-10-23)
Field: Materials Science — Materials Chemistry

Chemical shifts in molecular solids by machine learning Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the ability to calculate them, which comes at the cost of high accuracy first-principles calculations. Machine learning has recently emerged as a way to overcome the need for quantum chemical calculations, but for chemical shifts in solids it is hindered by the chemical and combinatorial space spanned by molecular solids, the strong dependency of chemical shifts on their environment, and the lack of an experimental database of shifts. We propose a machine learning method based on local environments to accurately predict chemical shifts of molecular solids and their polymorphs to within DFT accuracy. We also demonstrate that the trained model is able to determine, based on the match between experimentally measured and ML-predicted shifts, the structures of cocaine and the drug 4-[4-(2-adamantylcarbamoyl)-5-tert-butylpyrazol-1-yl]benzoic acid. Chemical shift ; Chemical space ; Molecular solid ; Crystal structure prediction ; Amorphous solid ; Chemical bond ; Machine learning ; Computer science ; Materials science ; Drug discovery ; Crystal structure ; Chemistry ; Molecule ; Crystallography ; Physical chemistry ; Organic chemistry X-ray Diffraction in Crystallography ; Advanced NMR Techniques and Applications ; Crystallography and molecular interactions
Authors: Chi Zhang | Ekaterina V. Vinogradova | Alexander M. Spokoyny | Stephen L. Buchwald | Bradley L. Pentelute
Source: Angewandte Chemie International Edition (review, 2018-11-06)
Field: Chemistry — Organic Chemistry

Arylation Chemistry for Bioconjugation Bioconjugation chemistry has been used to prepare modified biomolecules with functions beyond what nature intended. Central to these techniques is the development of highly efficient and selective bioconjugation reactions that operate under mild, biomolecule compatible conditions. Methods that form a nucleophile-sp<sup>2</sup> carbon bond show promise for creating bioconjugates with new modifications, sometimes resulting in molecules with unparalleled functions. Here we outline and review sulfur, nitrogen, selenium, oxygen, and carbon arylative bioconjugation strategies and their applications to modify peptides, proteins, sugars, and nucleic acids. Bioconjugation ; Biomolecule ; Chemistry ; Combinatorial chemistry ; Nucleic acid ; Nanotechnology ; Biochemistry ; Materials science Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Chemical Synthesis and Analysis
Authors: Volker L. Deringer | Noam Bernstein | Albert P. Bartók | Matthew J. Cliffe | Rachel N. Kerber | Lauren E. Marbella | Clare P. Grey | Stephen R. Elliott | Gábor Cśanyi
Source: The Journal of Physical Chemistry Letters (article, 2018-05-12)
Field: Materials Science — Materials Chemistry

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics Amorphous silicon ( a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 10<sup>11</sup> K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and <sup>29</sup>Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials. Molecular dynamics ; Silicon ; Amorphous silicon ; Dynamics (music) ; Amorphous solid ; Materials science ; Computer science ; Statistical physics ; Nanotechnology ; Chemical physics ; Physics ; Crystallography ; Computational chemistry ; Chemistry ; Crystalline silicon ; Optoelectronics Machine Learning in Materials Science ; Thin-Film Transistor Technologies ; X-ray Diffraction in Crystallography
Authors: Andrea Grisafi | Alberto Fabrizio | Benjamin Meyer | David M. Wilkins | Clémence Corminbœuf | Michele Ceriotti
Source: ACS Central Science (article, 2018-12-26)
Field: Materials Science — Materials Chemistry

Transferable Machine-Learning Model of the Electron Density The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems. Electronic structure ; Valence (chemistry) ; Linear scale ; Charge density ; Charge (physics) ; Density functional theory ; Scaling ; Computer science ; Statistical physics ; Chemical physics ; Physics ; Chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions