jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Enrico Cerrato | Umberto Barbero | Jorge A Gil Romero | Giorgio Quadri | Hernán Mejía‐Rentería | Francesco Tomassini | Fabio Ferrari | Ferdinando Varbella | Nieves Gonzalo | Javier Escaned
Source: Future Cardiology (review, 2019-05-14)
Field: Materials Science — Materials Chemistry

Magmaris™ Resorbable Magnesium Scaffold: state-of-art Review Bioresorbable scaffolds (BRS) have been advocated as the 'fourth revolution' in interventional cardiology because they could provide temporary scaffolding and then 'disappear' (resorb) potentially significantly improving coronary artery disease treatment. BRS technology has gradually matured, and there are many devices available worldwide, which are currently undergoing preclinical or clinical testing. Due to the concerns related to polylactide scaffolds, magnesium alloy is now one of the most promising resorbable technologies despite available evidences on its performances <i>in vivo</i> are limited to small observational studies. In this state-of-art review we present Magmaris™ (Biotronik AG, Buelach, Switzerland) magnesium-based BRS from bench to bedside, reviewing to date available clinical trial data and current recommendations for its optimal use in clinical practice. Medicine ; Scaffold ; Bioresorbable scaffold ; Magnesium ; Biomedical engineering ; Internal medicine ; Percutaneous coronary intervention ; Metallurgy ; Myocardial infarction Magnesium Oxide Properties and Applications ; Magnesium Alloys: Properties and Applications ; Materials Engineering and Processing
Authors: Scott T. Alsid | John F. Barry | Linh M. Pham | Jennifer M. Schloss | Michael F. O’Keeffe | Paola Cappellaro | Danielle A. Braje
Source: Physical Review Applied (article, 2019-10-01)
Field: Materials Science — Materials Chemistry

Photoluminescence Decomposition Analysis: A Technique to Characterize <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mrow><mml:mrow><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:mrow></mml:math>-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>V</mml:mi></mml:math> Creation in Diamond Nitrogen-vacancy centers in diamond are promising as sensitive vector magnetometers, quantum repeaters, and qubits, but realizing this potential has been limited by the diamond material itself. Here a comprehensive study addresses creation of nitrogen-vacancy centers, balancing the impact of irradiation and annealing on the quantum coherence properties of commercial material. Photoluminescence decomposition analysis is used to quantitatively determine the neutral-to-negative charge-state ratio, and for insight into the impact of electron irradiation on material quantum coherence properties. Photoluminescence ; Diamond ; Coherence (philosophical gambling strategy) ; Annealing (glass) ; Quantum ; Decomposition ; Coherence time Diamond and Carbon-based Materials Research ; Nanotechnology research and applications ; Tunneling and Rock Mechanics
Authors: Zhilei Wang | Yuuki Yokoyama | Tetsuhiko Onda | Yoshitaka Adachi | Zhong‐Chun Chen
Source: Advanced Electronic Materials (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Improved Thermoelectric Properties of Hot‐Extruded Bi–Te–Se Bulk Materials with Cu Doping and Property Predictions via Machine Learning Abstract Cu‐doped Bi 2 Te 2.85 Se 0.15 bulk thermoelectric materials are fabricated using a hot‐extrusion technique. The Cu atoms are found to intercalate into interstitial sites between the Te(1)–Te(1) layers, which results in a reduction in the carrier concentration, and thus increases in the related Seebeck coefficient and electrical resistivity and a decrease in the carrier thermal conductivity. A resulting ZT max value of 0.86 is obtained in the Cu 0.05 Bi 2 Te 2.85 Se 0.15 sample, which is 83% higher than that of the Cu‐free sample. As data‐driven materials science is becoming increasingly important for materials design, quantitative information on the processing, microstructure, and properties of the hot‐extruded materials is also estimated using a machine learning approach, where property predictions are conducted using an artificial neural network model. Moreover, an inverse exploration of the potential best material properties and their corresponding microstructure and processing is further attempted by using a Bayesian optimization algorithm. This study is expected to provide a new route to fabrication of high‐performance Bi 2 Te 3 ‐based thermoelectric materials with a focus on data‐driven materials design. Materials science ; Thermoelectric effect ; Microstructure ; Thermoelectric materials ; Seebeck coefficient ; Electrical resistivity and conductivity ; Doping ; Extrusion ; Thermal conductivity ; Composite material ; Nanotechnology ; Optoelectronics ; Thermodynamics ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Tim Würger | Christian Feiler | Félix Musil | Gregor B. Vonbun‐Feldbauer | Daniel Höche | Sviatlana V. Lamaka | Mikhail L. Zheludkevich | Robert H. Meißner
Source: Frontiers in Materials (article, 2019-04-05)
Field: Materials Science — Materials Chemistry

Data Science Based Mg Corrosion Engineering Magnesium exhibits a high potential for a variety of applications in areas such as transport, energy and medicine. However, untreated magnesium alloys are prone to corrosion, restricting their practical application. Therefore, it is necessary to develop new approaches that can prevent or control corrosion and degradation processes in order to adapt to the specific needs of the application. One potential solution is using corrosion inhibitors which are capable of drastically reducing the degradation rate as a result of interactions with the metal surface or components of the corrosive medium. As the sheer number of potential dissolution modulators makes it impossible to obtain a detailed atomistic understanding of the inhibition mechanisms for each additive, other measures for inhibition prediction are required. For this purpose, a concept is presented that combines corrosion experiments, machine learning, data mining, density functional theory calculations and molecular dynamics to estimate corrosion inhibition properties of still untested molecules. Concomitantly, this approach will provide a deeper understanding of the fundamental mechanisms behind the prevention of corrosion events in magnesium-based materials and enables more accurate continuum corrosion simulations. The presented concept facilitates the search for molecules with a positive or negative effect on the inhibition efficiency and could thus significantly contribute to the better control of magnesium / electrolyte interface properties. Corrosion ; Dissolution ; Magnesium ; Materials science ; Density functional theory ; Molecular dynamics ; Degradation (telecommunications) ; Electrolyte ; Nanotechnology ; Computer science ; Biochemical engineering ; Chemistry ; Metallurgy ; Computational chemistry ; Engineering ; Physical chemistry Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ryohei Eguchi | Naoaki Ono | Aki Morita | Tetsuo Katsuragi | Satoshi Nakamura | Ming Huang | Md. Altaf‐Ul‐Amin | Shigehiko Kanaya
Source: BMC Bioinformatics (article, 2019-07-09)
Field: Chemistry — Analytical Chemistry

Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified. In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network. Molecular similarity is a crucial metric in the analysis of qualitative structure–activity relationships. However, it is sometimes difficult for current fingerprint representations to emphasize specific features for the target problems efficiently. It is advantageous to allow the model to select the appropriate features according to data-driven decisions for extracting more useful information, which influences a classification or regression problem substantially. In this study, we applied a neural network architecture for undirected graph representation of molecules. By encoding a molecule as an abstract graph and applying "convolution" on the graph and training the weight of the neural network framework, the neural network can optimize feature selection for the training problem. By incorporating the effects from adjacent atoms recursively, graph convolutional neural networks can extract the features of latent atoms that represent chemical features of a molecule efficiently. In order to investigate alkaloid biosynthesis, we trained the network to distinguish the precursors of 566 alkaloids, which are almost all of the alkaloids whose biosynthesis pathways are known, and showed that the model could predict starting substances with an averaged accuracy of 97.5%. We have showed that our model can predict more accurately compared to the random forest and general neural network when the variables and fingerprints are not selected, while the performance is comparable when we carefully select 507 variables from 18000 dimensions of descriptors. The prediction of pathways contributes to understanding of alkaloid synthesis mechanisms and the application of graph based neural network models to similar problems in bioinformatics would therefore be beneficial. We applied our model to evaluate the precursors of biosynthesis of 12000 alkaloids found in various organisms and found power-low-like distribution. Convolutional neural network ; Computer science ; Graph ; DNA microarray ; Computational biology ; Artificial intelligence ; Biology ; Theoretical computer science ; Genetics ; Gene ; Gene expression Chromatography in Natural Products ; Computational Drug Discovery Methods ; Plant-based Medicinal Research
Authors: Zhuole Lu | Shwetank Yadav | Chandra Veer Singh
Source: Catalysis Science & Technology (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Predicting aggregation energy for single atom bimetallic catalysts on clean and O* adsorbed surfaces through machine learning models Machine learning models are successfully developed for simultaneous prediction of stability and adsorption energy at single-atom bimetallic sites. Bimetallic strip ; Adsorption ; Catalysis ; Atom (system on chip) ; Stability (learning theory) ; Energy (signal processing) ; Chemistry ; Materials science ; Chemical engineering ; Computer science ; Physical chemistry ; Machine learning ; Physics ; Engineering ; Organic chemistry ; Embedded system Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Alessandro Lunghi | Stefano Sanvito
Source: Science Advances (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity Computational studies of chemical processes taking place over extended size and time scales are inaccessible by electronic structure theories and can be tackled only by atomistic models such as force fields. These have evolved over the years to describe the most diverse systems. However, as we improve the performance of a force field for a particular physical/chemical situation, we are also moving away from a unified description. Here, we demonstrate that a unified picture of the covalent bond is achievable within the framework of machine learning-based force fields. Ridge regression, together with a representation of the atomic environment in terms of bispectrum components, can be used to map a general potential energy surface for molecular systems at chemical accuracy. This protocol sets the ground for the generation of an accurate and universal class of potentials for both organic and organometallic compounds with no specific assumptions on the chemistry involved. Covalent bond ; Force field (fiction) ; Representation (politics) ; Statistical physics ; Chemical bond ; Molecule ; Field (mathematics) ; Chemical physics ; Computer science ; Potential energy surface ; Computational chemistry ; Theoretical physics ; Physics ; Chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ivan A. Kruglov | A. V. Yanilkin | Artem R. Oganov | Pavel Korotaev
Source: Physical review. B./Physical review. B (article, 2019-11-12)
Field: Materials Science — Materials Chemistry

Phase diagram of uranium from<i>ab initio</i>calculations and machine learning Experimental studies of materials at extreme conditions are challenging, and as a consequence, P-T phase diagrams are still unknown for many elements and materials. In this work, we present the P-T phase diagram of uranium calculated up to extreme conditions. First, we searched for possible crystal structures using the evolutionary algorithm USPEX. Their free energies were then calculated using thermodynamic integration (TI) and temperature-dependent effective potential techniques. TI was performed using molecular dynamics, employing a machine learning (ML) force field trained on energies and forces from density-functional calculations at the generalized gradient approximation level. The prediction error of the ML force field for the energy was less than 10 meV/atom. Using thermodynamic perturbation theory (including first and second order corrections), from the free energies of the ML force field, we obtained free energies and phase diagram at the level of quality of the underlying density-functional calculations at pressures up to 800 GPa and temperatures up to 16 000 K. Phase diagram ; Density functional theory ; Perturbation theory (quantum mechanics) ; Ab initio ; Atom (system on chip) ; Uranium ; Ab initio quantum chemistry methods ; Thermodynamic integration ; Force field (fiction) ; Thermodynamics ; CALPHAD ; Materials science ; Work (physics) ; Molecular dynamics ; Physics ; Phase (matter) ; Molecule ; Quantum mechanics ; Computer science ; Nuclear physics Nuclear Materials and Properties ; Machine Learning in Materials Science ; High-pressure geophysics and materials
Authors: William Luna Salgado | Roos Sophia de Freitas Dam | Tâmara P. Teixeira | C.C. Conti | César Marques Salgado
Source: Radiation Physics and Chemistry (article, 2019-11-02)
Field: Chemistry — Analytical Chemistry

Application of artificial intelligence in scale thickness prediction on offshore petroleum using a gamma-ray densitometer Artificial neural network ; Detector ; Pipeline transport ; Densitometer ; Petroleum engineering ; Submarine pipeline ; Scale (ratio) ; Logging while drilling ; Pipeline (software) ; Petroleum ; Materials science ; Nuclear engineering ; Environmental science ; Marine engineering ; Computer science ; Geology ; Optics ; Engineering ; Mechanical engineering ; Artificial intelligence ; Physics ; Geotechnical engineering ; Drilling Petroleum Processing and Analysis ; Nuclear Physics and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Valerio Adinolfi | Lanxia Cheng | Mario Laudato | Ryan Clarke | V. Narasimhan | Simone Balatti | Son Hoang | Karl A. Littau
Source: ACS Nano (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Composition-Controlled Atomic Layer Deposition of Phase-Change Memories and Ovonic Threshold Switches with High Performance Chalcogenide compounds are the main characters in a revolution in electronic memories. These materials are used to produce ultrafast ovonic threshold switches (OTSs) with good selectivity and moderate leakage current and phase-change memories (PCMs) with excellent endurance and short read/write times when compared with state-of-the-art flash-NANDs. The combination of these two electrical elements is used to fabricate nonvolatile memory arrays with a write/access time orders of magnitude shorter than that of state-of-the-art flash-NANDs. These devices have a pivotal role for the advancement of fields such as artificial intelligence, machine learning, and big-data. Chalcogenide films, at the moment, are deposited by using physical vapor deposition (PVD) techniques that allow for fine control over the stoichiometry of solid solutions but fail in providing the conformality required for developing large-memory-capacity integrated 3D structures. Here we present conformal ALD chalcogenide films with control over the composition of germanium, antimony, and tellurium (GST). By developing a technique to grow elemental Te we demonstrate the ability to deposit conformal, smooth, composition-controlled GST films. We present a thorough physical and chemical characterization of the solids and an in-depth electrical test. We demonstrate the ability to produce both OTS and PCM materials. GeTe<sub>4</sub> OTSs exhibit fast switching times of ∼13 ns. Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> ALD PCMs exhibit a wide memory window exceeding two orders of magnitude, short write times (∼100 ns), and a reset current density as low as ∼10<sup>7</sup> A/cm<sup>2</sup>-performance matching or improving upon state-of-the-art PVD PCM devices. Atomic layer deposition ; Materials science ; Layer (electronics) ; Phase-change memory ; Optoelectronics ; Deposition (geology) ; Nanotechnology ; Phase (matter) ; Chemistry Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Semiconductor materials and devices
Authors: Marc Brockschmidt
Source: arXiv (Cornell University) (preprint, 2019-06-28)
Field: Materials Science — Materials Chemistry

GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM). Many standard GNN variants propagate information along the edges of a graph by computing "messages" based only on the representation of the source of each edge. In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information. Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods. Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature. Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks. Hyperparameter ; Computer science ; Graph ; Pattern recognition (psychology) ; Representation (politics) ; Feature (linguistics) ; Enhanced Data Rates for GSM Evolution ; Implementation ; Artificial neural network ; Artificial intelligence ; Algorithm ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sae Dieb | Shenghong Ju | Junichiro Shiomi | Koji Tsuda
Source: MRS Communications (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Monte Carlo tree search for materials design and discovery Abstract Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target property. Since the number of candidates can be exponentially proportional to the structure determination variables, the optimal structure must be obtained efficiently. Recently, inspired by its success in the Go computer game, several approaches have applied Monte Carlo tree search (MCTS) to solve optimization problems in natural sciences including materials science. In this paper, we briefly reviewed applications of MCTS in materials design and discovery, and analyzed its future potential. Monte Carlo tree search ; Monte Carlo method ; Tree (set theory) ; Computer science ; Property (philosophy) ; Space (punctuation) ; Mathematical optimization ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Paweł Pomastowski | Bogusław Buszewski
Source: Nanomaterials (review, 2019-02-14)
Field: Chemistry — Spectroscopy

Complementarity of Matrix- and Nanostructure-Assisted Laser Desorption/Ionization Approaches In recent years, matrix-assisted laser desorption/ionization (MALDI) has become the main tool for the study of biological macromolecules, such as protein nano-machines, especially in the determination of their molecular masses, structure, and post-translational modifications. A key role in the classical process of desorption and ionization of the sample is played by a matrix, usually a low-molecular weight weak organic acid. Unfortunately, the interpretation of mass spectra in the mass range of below <i>m/z</i> 500 is difficult, and hence the analysis of low molecular weight compounds in a matrix-assisted system is an analytical challenge. Replacing the classical matrix with nanomaterials, e.g., silver nanoparticles, allows improvement of the selectivity and sensitivity of spectrometric measurement of biologically important small molecules. Nowadays, the nanostructure-assisted laser desorption/ionization (NALDI) approach complements the classic MALDI in the field of modern bioanalytics. In particular, the aim of this work is to review the recent advances in MALDI and NALDI approaches. Nanostructure ; Mass spectrometry ; Matrix-assisted laser desorption/ionization ; Ionization ; Desorption ; Nanomaterials ; Nanotechnology ; Matrix (chemical analysis) ; MALDI imaging ; Materials science ; Chemistry ; Laser ; Soft laser desorption ; Chemical physics ; Analytical Chemistry (journal) ; Chromatography ; Physical chemistry ; Physics ; Organic chemistry ; Adsorption ; Optics ; Ion Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Ion-surface interactions and analysis
Authors: Stephanie T. P. Mezger | Alma M.A. Mingels | Otto Bekers | Berta Cillero‐Pastor | Ron M. A. Heeren
Source: Analytical and Bioanalytical Chemistry (article, 2019-04-12)
Field: Chemistry — Spectroscopy

Trends in mass spectrometry imaging for cardiovascular diseases Mass spectrometry imaging (MSI) is a widely established technology; however, in the cardiovascular research field, its use is still emerging. The technique has the advantage of analyzing multiple molecules without prior knowledge while maintaining the relation with tissue morphology. Particularly, MALDI-based approaches have been applied to obtain in-depth knowledge of cardiac (dys)function. Here, we discuss the different aspects of the MSI protocols, from sample handling to instrumentation used in cardiovascular research, and critically evaluate these methods. The trend towards structural lipid analysis, identification, and "top-down" protein MSI shows the potential for implementation in (pre)clinical research and complementing the diagnostic tests. Moreover, new insights into disease progression are expected and thereby contribute to the understanding of underlying mechanisms related to cardiovascular diseases. Identification (biology) ; Mass spectrometry ; Mass spectrometry imaging ; Computational biology ; Instrumentation (computer programming) ; Disease ; Medicine ; Data science ; Computer science ; Bioinformatics ; Nanotechnology ; Chemistry ; Internal medicine ; Biology ; Materials science ; Chromatography Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications