jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nailiang Yang | Hongfei Cheng | Xiaozhi Liu | Qinbai Yun | Ye Chen | Bing Li | Bo Chen | Zhicheng Zhang | Xiaoping Chen | Qipeng Lu | Jingtao Huang | Ying Huang | Yun Zong | Yanhui Yang | Lin Gu | Hua Zhang
Source: Advanced Materials (article, 2018-08-14)
Field: Chemistry — Organic Chemistry

Amorphous/Crystalline Hetero‐Phase Pd Nanosheets: One‐Pot Synthesis and Highly Selective Hydrogenation Reaction Similar to heterostructures composed of different materials, possessing unique properties due to the synergistic effect between different components, the crystal-phase heterostructures, one variety of hetero-phase structures, composed of different crystal phases in monometallic nanomaterials are herein developed, in order to explore crystal-phase-based applications. As novel hetero-phase structures, amorphous/crystalline heterostructures are highly desired, since they often exhibit unique properties, and hold promise in various applications, but these structures have rarely been studied in noble metals. Herein, via a one-pot wet-chemical method, a series of amorphous/crystalline hetero-phase Pd nanosheets is synthesized with different crystallinities for the catalytic 4-nitrostyrene hydrogenation. The chemoselectivity and activity can be fine-tuned by controlling the crystallinity of the as-synthesized Pd nanosheets. This work might pave the way to preparing various hetero-phase nanostructures for promising applications. Materials science ; Crystallinity ; Amorphous solid ; Nanomaterials ; Phase (matter) ; Heterojunction ; Nanotechnology ; Crystal (programming language) ; Nanostructure ; Chemical engineering ; Catalysis ; Crystal structure ; Chemoselectivity ; Organic chemistry ; Composite material ; Optoelectronics ; Chemistry Nanomaterials for catalytic reactions ; Nanocluster Synthesis and Applications ; Graphene research and applications
Authors: Mark D. Allendorf | Z. Hulvey | Thomas Gennett | Alauddin Ahmed | Tom Autrey | Jeffrey S. Camp | Eun Seon Cho | Hiroyasu Furukawa | Maciej Harańczyk | Martin Head‐Gordon | Sohee Jeong | Abhi Karkamkar | Di‐Jia Liu | Jeffrey R. Long | Katie R. Meihaus | Iffat Nayyar | Roman Nazarov | Donald J. Siegel | Vitalie Stavila | Jeffrey J. Urban | Srimukh Prasad Veccham | Brandon C. Wood
Source: Energy & Environmental Science (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

An assessment of strategies for the development of solid-state adsorbents for vehicular hydrogen storage We assess the strengths and weaknesses of strategies for creating nanoporous hydrogen storage sorbents. Hydrogen storage ; Nanoporous ; Strengths and weaknesses ; Solid-state ; Hydrogen ; Adsorption ; Solid hydrogen ; Computer science ; Nanotechnology ; Environmental science ; Materials science ; Chemistry ; Engineering ; Psychology ; Engineering physics ; Organic chemistry Hydrogen Storage and Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Chenhao Cui | Tom Fearn
Source: Chemometrics and Intelligent Laboratory Systems (article, 2018-07-20)
Field: Chemistry — Analytical Chemistry

Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration Partial least squares regression ; Convolutional neural network ; Preprocessor ; Multivariate statistics ; Computer science ; Calibration ; Pattern recognition (psychology) ; Artificial intelligence ; Regression ; Near-infrared spectroscopy ; Machine learning ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Martin Salinga | Benedikt Kersting | Ider Ronneberger | Vara Prasad Jonnalagadda | Xuan Thang Vu | Manuel Le Gallo | Iason Giannopoulos | Oana Cojocaru-Mirédin | Riccardo Mazzarello | Abu Sebastian
Source: Nature Materials (article, 2018-06-15)
Field: Materials Science — Materials Chemistry

Monatomic phase change memory Neuromorphic engineering ; Monatomic ion ; Phase-change memory ; Scaling ; Stability (learning theory) ; Memristor ; Mixing (physics) ; Phase (matter) ; Electronics Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Rajan Sharma Bhattarai | Rinda Devi Bachu | Sai H. S. Boddu | Sarit B. Bhaduri
Source: Pharmaceutics (review, 2018-12-24)
Field: Materials Science — Biomaterials

Biomedical Applications of Electrospun Nanofibers: Drug and Nanoparticle Delivery The electrospinning process has gained popularity due to its ease of use, simplicity and diverse applications. The properties of electrospun fibers can be controlled by modifying either process variables (e.g., applied voltage, solution flow rate, and distance between charged capillary and collector) or polymeric solution properties (e.g., concentration, molecular weight, viscosity, surface tension, solvent volatility, conductivity, and surface charge density). However, many variables affecting electrospinning are interdependent. An optimized electrospinning process is one in which these parameters remain constant and continuously produce nanofibers consistent in physicochemical properties. In addition, nozzle configurations, such as single nozzle, coaxial, multi-jet electrospinning, have an impact on the fiber characteristics. The polymeric solution could be aqueous, a polymeric melt or an emulsion, which in turn leads to different types of nanofiber formation. Nanofiber properties can also be modified by polarity inversion and by varying the collector design. The active moiety is incorporated into polymeric fibers by blending, surface modification or emulsion formation. The nanofibers can be further modified to deliver multiple drugs, and multilayer polymer coating allows sustained release of the incorporated active moiety. Electrospun nanofibers prepared from polymers are used to deliver antibiotic and anticancer agents, DNA, RNA, proteins and growth factors. This review provides a compilation of studies involving the use of electrospun fibers in biomedical applications with emphasis on nanoparticle-impregnated nanofibers. Electrospinning ; Nanofiber ; Materials science ; Nanoparticle ; Polymer ; Emulsion ; Chemical engineering ; Nanotechnology ; Drug delivery ; Surface modification ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: Christian Adlhart | Joanna Verran | Nuno F. Azevedo | Hülya Ölmez | Minna M. Keinänen‐Toivola | Isabel C. Gouveia | L. F. Melo | F. R. L. Crijns
Source: Journal of Hospital Infection (review, 2018-02-02)
Field: Chemistry — Organic Chemistry

Surface modifications for antimicrobial effects in the healthcare setting: a critical overview The spread of infections in healthcare environments is a persistent and growing problem in most countries, aggravated by the development of microbial resistance to antibiotics and disinfectants. In addition to indwelling medical devices (e.g. implants, catheters), such infections may also result from adhesion of microbes either to external solid-water interfaces such as shower caps, taps, drains, etc., or to external solid-gas interfaces such as door handles, clothes, curtains, computer keyboards, etc. The latter are the main focus of the present work, where an overview of antimicrobial coatings for such applications is presented. This review addresses well-established and novel methodologies, including chemical and physical functional modification of surfaces to reduce microbial contamination, as well as the potential risks associated with the implementation of such anticontamination measures. Different chemistry-based approaches are discussed, for instance anti-adhesive surfaces (e.g. superhydrophobic, zwitterions), contact-killing surfaces (e.g. polymer brushes, phages), and biocide-releasing surfaces (e.g. triggered release, quorum sensing-based systems). The review also assesses the impact of topographical modifications at distinct dimensions (micrometre and nanometre orders of magnitude) and the importance of applying safe-by-design criteria (e.g. toxicity, contribution for unwanted acquisition of antimicrobial resistance, long-term stability) when developing and implementing antimicrobial surfaces. Medicine ; Antimicrobial ; Health care ; Intensive care medicine ; Microbiology Antimicrobial agents and applications ; Surgical Sutures and Adhesives ; Graphene and Nanomaterials Applications
Authors: Keitaro Yamashita | Kunio Hirata | Masaki Yamamoto
Source: Acta Crystallographica Section D Structural Biology (article, 2018-04-24)
Field: Materials Science — Materials Chemistry

<i>KAMO</i>: towards automated data processing for microcrystals In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5-10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. Computer science ; Data mining ; Data set ; Cluster analysis ; Outlier ; Data processing ; Wedge (geometry) ; Pipeline (software) ; Set (abstract data type) ; Artificial intelligence ; Database ; Physics ; Optics Enzyme Structure and Function ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Giulio Imbalzano | Andrea Anelli | Daniele Giofré | Sinja Klees | Jörg Behler | Michele Ceriotti
Source: The Journal of Chemical Physics (article, 2018-04-30)
Field: Materials Science — Materials Chemistry

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials Machine learning of atomic-scale properties is revolutionizing molecular modeling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs. The accuracy, speed, and reliability of machine learning potentials, however, depend strongly on the way atomic configurations are represented, i.e., the choice of descriptors used as input for the machine learning method. The raw Cartesian coordinates are typically transformed in "fingerprints," or "symmetry functions," that are designed to encode, in addition to the structure, important properties of the potential energy surface like its invariances with respect to rotation, translation, and permutation of like atoms. Here we discuss automatic protocols to select a number of fingerprints out of a large pool of candidates, based on the correlations that are intrinsic to the training data. This procedure can greatly simplify the construction of neural network potentials that strike the best balance between accuracy and computational efficiency and has the potential to accelerate by orders of magnitude the evaluation of Gaussian approximation potentials based on the smooth overlap of atomic positions kernel. We present applications to the construction of neural network potentials for water and for an Al-Mg-Si alloy and to the prediction of the formation energies of small organic molecules using Gaussian process regression. Artificial neural network ; Process (computing) ; Gaussian ; Permutation (music) ; Gaussian process ; Energy (signal processing) ; Fraction (chemistry) ; Cartesian coordinate system ; Surface (topology) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: A.A.A. Darwish | M. Rashad | Hatem A. Al‐Aoh
Source: Dyes and Pigments (article, 2018-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Methyl orange adsorption comparison on nanoparticles: Isotherm, kinetics, and thermodynamic studies Adsorption ; Endothermic process ; Non-blocking I/O ; Freundlich equation ; Langmuir ; Kinetics ; Thermodynamics ; Methyl orange ; Langmuir adsorption model ; Chemistry ; Materials science ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Catalysis Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions ; Copper-based nanomaterials and applications
Authors: Michael Gastegger | Ludwig Schwiedrzik | Marius R. Bittermann | Florian Berzsenyi | Philipp Marquetand
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy. Generalization ; Parametrization (atmospheric modeling) ; Symmetry (geometry) ; Artificial neural network ; Atom (system on chip) ; Scaling ; Set (abstract data type) ; Computer science ; Simple (philosophy) ; Algorithm ; Artificial intelligence ; Topology (electrical circuits) ; Biological system ; Mathematics ; Physics ; Geometry ; Quantum mechanics ; Mathematical analysis ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Nicholas Lubbers | Justin S. Smith | Kipton Barros
Source: The Journal of Chemical Physics (article, 2018-03-19)
Field: Materials Science — Materials Chemistry

Hierarchical modeling of molecular energies using a deep neural network We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network-a composition of many nonlinear transformations-acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty. Artificial neural network ; Representation (politics) ; Deep neural networks ; Energy (signal processing) ; Nonlinear system ; Particle (ecology) ; State (computer science) ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Elisa Borfecchia | Pablo Beato | Stian Svelle | Unni Olsbye | Carlo Lamberti | Silvia Bordiga
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Materials Chemistry

Cu-CHA – a model system for applied selective redox catalysis We review the structural chemistry and reactivity of copper-exchanged molecular sieves with chabazite (CHA) topology, as an industrially applied catalyst in ammonia mediated reduction of harmful nitrogen oxides (NH3-SCR) and as a general model system for red-ox active materials (also the recent results in the direct conversion of methane to methanol are considered). Notwithstanding the apparent structural simplicity of the material, a crystalline zeolite with only one crystallographically independent T site, the Cu-SSZ-13 catalyst reveals a high degree of complexity that has been decrypted by state of the art characterization tools. From the reviewed data, the following important aspects in the understanding of the Cu-SSZ-13 catalyst clearly emerged: (i) the structural dynamics of the Cu-species require precise control of the environmental conditions during activation and characterization; (ii) the availability of a large library of well-defined catalysts with different Si/Al and Cu/Al compositional ratios is key in unravelling the red-ox properties of the active Cu sites; (iii) a multi-technique approach is required, combining complementary techniques able to provide independent structural, electronic and vibrational information; (iv) synchrotron radiation based techniques (EXAFS, XANES, XES and time-resolved powder XRD) played a relevant role; (v) operando methodology (possibly supported by advanced chemometric approaches) is essential in obtaining structure-reactivity relations; (vi) the support of theoretical studies has been indispensable for the interpretation of the experimental output from characterization and for a critical assessment of mechanistic models. The old literature that classified Cu-exchanged zeolites in the category of single-site catalysts has been partially disproved by the recent advanced studies where it has been shown that the active site in the low temperature NH3-SCR catalyst is a mobile Cu-molecular entity that "lives in symbiosis" with an inorganic solid framework. Only in the high temperature NH3-SCR regime do the mobile Cu-species lose their ligands and find docking sites at the internal walls of the zeolite framework, thus reflecting the idea of a single-site catalyst. After a brief introduction, the review is divided into three main parts devoted to characterization (Section 2), reactivity (Section 3), and industrial applications (Section 4), followed by some concluding remarks and providing a perspective of the field. Chabazite ; Chemistry ; Catalysis ; Molecular sieve ; Redox ; Reactivity (psychology) ; Methanol ; Methane ; Ammonia ; Copper ; Inorganic chemistry ; Selective catalytic reduction ; Selective reduction ; Combinatorial chemistry ; Organic chemistry ; Zeolite Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Kan Yao | Rohit Unni | Yuebing Zheng
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks. Nanophotonics ; Photonics ; Artificial neural network ; Field (mathematics) ; Exploit ; Deep learning ; Computational intelligence Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Neural Networks and Reservoir Computing
Authors: Daniele Dragoni | Thomas D. Daff | Gábor Csányi | Nicola Marzari
Source: Physical Review Materials (article, 2018-01-30)
Field: Materials Science — Materials Chemistry

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron We show that the Gaussian Approximation Potential (GAP) machine-learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and stresses obtained from density-functional theory in the generalized-gradient approximation, and comprises approximately 150,000 local atomic environments, ranging from pristine and defected bulk configurations to surfaces and generalized stacking faults with different crystallographic orientations. We find the structural, vibrational, and thermodynamic properties of the GAP model to be in excellent agreement with those obtained directly from first-principles electronic-structure calculations. There is good transferability to quantities, such as Peierls energy barriers, which are determined to a large extent by atomic configurations that were not part of the training set. We observe the benefit and the need of using highly converged electronic-structure calculations to sample a target potential energy surface. The end result is a systematically improvable potential that can achieve the same accuracy of density-functional theory calculations, but at a fraction of the computational cost. Density functional theory ; Materials science ; Ferromagnetism ; Stacking ; Transferability ; Local-density approximation ; Condensed matter physics ; Electronic structure ; Magnetism ; Hybrid functional ; Machine learning ; Computational chemistry ; Computer science ; Physics ; Nuclear magnetic resonance ; Chemistry Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; X-ray Diffraction in Crystallography