jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Filip Lisiecki | Justyna Rychły-Gruszecka | Piotr Kuświk | Hubert Głowiński | Jarosław W. Kłos | Felix Groß | Iuliia Bykova | Markus Weigand | Mateusz Zelent | E. Goering | Gisela Schütz | G. Gubbiotti | Maciej Krawczyk | F. Stobiecki | J. Dubowik | Joachim Gräfe
Source: Physical Review Applied (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

Reprogrammability and Scalability of Magnonic Fibonacci Quasicrystals Magnonic quasicrystals can be used to manipulate spin waves, offering possibilities beyond those of periodic magnonic crystals. The authors investigate one-dimensional magnonic Fibonacci quasicrystals and demonstrate the existence of collective spin waves over a broad range of wave vectors. The spin-wave spectra here are tunable by changing magnetic field amplitude (for continuous band-structure adjustment), magnetization configuration (for reprogrammability), or the dimensions of the elements (for scalability). Beyond being fundamentally interesting, these properties show that magnonic quasicrystals are promising for tomorrow's spintronic, microwave, and magnonic technologies. Quasicrystal ; Fibonacci number ; Spin wave ; Spintronics ; Condensed matter physics ; Magnetization ; Magnonics ; Physics ; Microwave ; Field (mathematics) ; Magnetic field ; Electron ; Quantum mechanics ; Spin polarization ; Spin Hall effect ; Ferromagnetism ; Mathematics Quasicrystal Structures and Properties ; Magnetic properties of thin films ; Metamaterials and Metasurfaces Applications
Authors: Hongda Yang | Qingguo Chen | Xinyu Wang | Minghe Chi | Heqian Liu | Xin Ning
Source: Polymers (article, 2019-08-16)
Field: Materials Science — Materials Chemistry

Dielectric and Thermal Conductivity of Epoxy Resin Impregnated Nano-h-BN Modified Insulating Paper Epoxy resin-impregnated insulation paper (RIP) composites are used as the inner insulation of dry condenser bushing in the ultra-high voltage direct current (UHVDC) power transmission system. To improve the dielectric properties and heat conductivity of RIP, hexagonal boron nitride (h-BN) nano-flakes are added to the insulation paper at concentrations of 0-50 wt % before impregnation with pure epoxy resin. X-ray diffraction (XRD), scanning electron microscopy (SEM) observations, thermal conductivity as well as the typical dielectric properties of direct current (DC) volume conductivity. DC breakdown strength and space charge characteristics were obtained. The maximum of nano-h-BN modified heat conductivity reach 0.478 W/(m·K), increased by 139% compared with unmodified RIP. The DC breakdown electric field strength of the nano-h-BN modified RIP does not reduce much. The conductivity of nano-h-BN modified is less sensitive to temperature. As well, the space charge is suppressed when the content is 50 wt %. Therefore, the nano-h-BN modified RIP is potentially useful in practical dry DC bushing application. Epoxy ; Materials science ; Composite material ; Dielectric ; Thermal conductivity ; Nano- High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Advanced Sensor and Energy Harvesting Materials
Authors: Shuming Zeng | Geng Li | Yinchang Zhao | Ruirui Wang | Jun Ni
Source: The Journal of Physical Chemistry C (article, 2019-02-06)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Design of Materials with Target Elastic Properties A set of universal descriptors which combines atomic properties with crystal fingerprint are presented to build interpretable models for elastic property prediction. Using the well-performed model, 100 materials with large predicted elastic moduli are screened out and then validated by the first-principles calculations. When performing projection analysis, we find that compounds with large and small elastic moduli are clearly divided into two parts by the average value of volume and atomization enthalpy (ΔHatomic), and the relation between them is given by two discriminant equations, suggesting that compounds composed of elements with large ΔHatomic are potential large elastic moduli materials. Following this rule, we design several new stable materials like ReTcB4 and ReB which have high elastic moduli. This method is valuable for high-throughput screening and material design. Elastic modulus ; Moduli ; Elastic net regularization ; Set (abstract data type) ; Projection (relational algebra) ; Materials science ; Computer science ; Mathematics ; Physics ; Composite material ; Algorithm ; Artificial intelligence ; Feature selection Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Hiroya Nakata | Shandan Bai
Source: Journal of Computational Chemistry (article, 2019-04-11)
Field: Materials Science — Materials Chemistry

Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the k-nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an α-Al<sub>2</sub> O<sub>3</sub> crystal. The crystal structure of α-Al<sub>2</sub> O<sub>3</sub> was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (11 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mover><mml:mn>2</mml:mn> <mml:mo>¯</mml:mo></mml:mover> </mml:math> 0) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process. © 2019 Wiley Periodicals, Inc. ReaxFF ; Force field (fiction) ; Molecular dynamics ; Chemical vapor deposition ; Computer science ; Materials science ; Chemistry ; Computational chemistry ; Nanotechnology ; Artificial intelligence ; Interatomic potential Machine Learning in Materials Science ; Semiconductor materials and devices ; Thermal properties of materials
Authors: Srikanth Patala
Source: Computational Materials Science (article, 2019-03-09)
Field: Materials Science — Metals and Alloys

Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning Misorientation ; Grain boundary ; Computer science ; Orientation (vector space) ; Property (philosophy) ; Artificial intelligence ; Materials science ; Machine learning ; Geometry ; Crystallography ; Chemistry ; Mathematics ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Cunzhi Zhang | Qiang Sun
Source: Journal of Applied Physics (article, 2019-09-09)
Field: Materials Science — Materials Chemistry

Gaussian approximation potential for studying the thermal conductivity of silicene Due to the compatibility with the well-developed Si-based semiconductor technology, the properties of silicene and silicene-based materials have attracted tremendous attention. Among them, the thermal conductivity (TC) is of special importance for electronic devices. However, unlike graphene, the poor quality of empirical potentials hinders the reliable evaluation of TC for silicene using molecular dynamics (MD). Here, we present a Gaussian approximation potential (GAP) for silicene based on ab initio derived training data. The potential can precisely describe the geometries, mechanical properties, as well as phonon dispersion of free-standing sheet, outperforming any other empirical ones. Using sinusoidal approach-to-equilibrium MD simulations based on the GAP potential, the TC of silicene is found to be 32.4±2.9W/mK at room temperature. Importantly, our result achieves a good agreement with Boltzmann transport equation (BTE) based first-principles predictions (∼30W/mK), such that the TC value of silicene is confirmed via both MD and BTE; thus, we prove that the accuracy of machine learning potentials, like GAP, can enable a faithful prediction of TC at a density functional theory (DFT) level. Silicene ; Thermal conductivity ; Phonon ; Graphene ; Density functional theory ; Materials science ; Condensed matter physics ; Gaussian ; Statistical physics ; Ab initio ; Boltzmann equation ; Nanotechnology ; Thermodynamics ; Computational chemistry ; Physics ; Quantum mechanics ; Chemistry Graphene research and applications ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Tuo He | Lichao Jiao | Alex C. Wiedenhoeft | Yafang Yin
Source: Planta (article, 2019-03-01)
Field: Chemistry — Organic Chemistry

Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood DNA barcoding ; Biology ; Barcode ; Identification (biology) ; NdhF ; Artificial intelligence ; Machine learning ; Tree (set theory) ; Computer science ; Botany ; Ecology ; Chloroplast DNA ; Mathematics ; Phylogenetic tree Wood and Agarwood Research ; Identification and Quantification in Food ; Genomics and Phylogenetic Studies
Authors: Merve Özkan | Alp Karakoç | Maryam Borghei | Jenny Wiklund | Orlando J. Rojas | Jouni Paltakari
Source: Polymer Composites (article, 2019-04-04)
Field: Materials Science — Biomaterials

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies Nowadays, modern nanomaterial research is complemented by machine learning methods to reduce experimental costs and process time. With this motivation, here, we implemented artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) methods to predict the mechanical properties of three‐component nanocomposite films consisting of polyvinyl alcohol (PVA) crosslinked 2,2,6,6‐tetramethylpiperidine‐1‐oxyl (TEMPO) oxidized cellulose nanofibers (TOCNFs) and either ammonium zirconium carbonate (AZC) or glyoxal (Gx) using the mechanical properties of mono‐component TOCNF films and two‐component nanocomposites containing PVA, AZC, or Gx‐crosslinked TOCNF as the input of prediction system. Prediction methods were evaluated with performance indicators and experimental data. Overall, MLR performed with least accuracy, whereas ANN prediction displayed the lowest error followed closely by RF. Additionally, the physically or/and chemically crosslinked hybrid films with optimized amount of crosslinkers resulted in structures with a strength to rupture that was significantly higher than that of the pure nanocellulose films (increases of up to ~90% in tensile strength and ~70% in Young's modulus). POLYM. COMPOS., 40:4013–4022, 2019. © 2019 Society of Plastics Engineers Nanocellulose ; Materials science ; Nanocomposite ; Ultimate tensile strength ; Composite material ; Polyvinyl alcohol ; Cellulose ; Chemical engineering Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Natural Fiber Reinforced Composites
Authors: Jonathan Lym | Geun Ho Gu | Yousung Jung | Dionisios G. Vlachos
Source: The Journal of Physical Chemistry C (article, 2019-07-12)
Field: Materials Science — Materials Chemistry

Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects Coverage effects, known also as lateral interactions, are often important in surface processes, but their study via exhaustive density functional theory (DFT) is impractical because of the large configurational degrees of freedom. The cluster expansion (CE) is the most popular surrogate model accounting for coverage effects but suffers from slow convergence, its linear form, and its tendency to be biased toward the selection of smaller clusters. We develop a novel lattice convolutional neural network (LCNN) that improves upon some of CE’s limitations and exhibits better performance (test RMSE of 4.4 meV/site) compared to state-of-the-art methods, such as the CE assisted by a genetic algorithm and the convolution operation of the crystal graph convolutional neural network (test RMSE of 5.5 and 6.8 meV/site, respectively) by 20–30%. Furthermore, LCNN can outperform other methods with less training data, implying accuracy with less DFT calculations. We analyze the van der Waals interaction via visualization of the hidden representation of the adsorbate lattice system in terms of individual site formation energies. Convolutional neural network ; Computer science ; Lattice (music) ; Materials science ; Statistical physics ; Artificial intelligence ; Physics ; Acoustics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Moritz Hoffmann | Frank Noé
Source: arXiv (Cornell University) (preprint, 2019-10-07)
Field: Materials Science — Materials Chemistry

Generating valid Euclidean distance matrices Generating point clouds, e.g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task. Meanwhile, neural networks utilizing symmetry invariant layers have been shown to be able to optimize their training objective in a data-efficient way. In this spirit, we present an architecture which allows to produce valid Euclidean distance matrices, which by construction are already invariant under rotation and translation of the described object. Motivated by the goal to generate molecular structures in Cartesian space, we use this architecture to construct a Wasserstein GAN utilizing a permutation invariant critic network. This makes it possible to generate molecular structures in a one-shot fashion by producing Euclidean distance matrices which have a three-dimensional embedding. Invariant (physics) ; Embedding ; Euclidean distance matrix ; Translation (biology) ; Computer science ; Euclidean space ; Euclidean geometry ; Rotation (mathematics) ; Euclidean distance ; Mathematics ; Pure mathematics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques ; Handwritten Text Recognition Techniques
Authors: Benyamin Motevalli | Amanda Parker | Baichuan Sun | Amanda S. Barnard
Source: Nano Futures (article, 2019-12-01)
Field: Materials Science — Materials Chemistry

The representative structure of graphene oxide nanoflakes from machine learning Abstract In this paper we revisit the structure of graphene oxide, and determine the pure and truly representative structures for graphene nanoflakes using machine learning. Using 20 396 random configurations relaxed at the electronic structure level, we observe the presence of hydroxyl, ether, double bonds, aliphatic (cyclohexane) disruption, defects and significant out-of-plane distortions that go beyond the Lerf–Klinowski model. Based on an diverse list of 224 chemical, structural and topological features we identify 25 archetypal ‘pure’ graphene oxide structures which capture all of the complexity and diversity of the entire data set; and three prototypes that are the truly representative averages in 224-dimensional space. Together these 28 structures, which are shown to be largely robust against changes in thermochemical conditions modeled using ab initio thermodynamics, can be downloaded and used collectively as a small data set for with a fraction of the computational cost in future work, or independently as an exemplar of graphene oxide with the required oxidation. Graphene ; Oxide ; Ab initio ; Cyclohexane ; Materials science ; Set (abstract data type) ; Nanotechnology ; Computational chemistry ; Computer science ; Machine learning ; Chemical physics ; Chemistry ; Organic chemistry Graphene research and applications ; Graphene and Nanomaterials Applications ; Machine Learning in Materials Science
Authors: Carlos Mera Acosta | A. Fazzio | Gustavo M. Dalpian
Source: npj Quantum Materials (article, 2019-08-07)
Field: Materials Science — Materials Chemistry

Zeeman-type spin splitting in nonmagnetic three-dimensional compounds Abstract Despite its potential for device application, the nonmagnetic Zeeman effect has only been predicted and observed in two-dimensional compounds. We demonstrate that noncentrosymmetric three-dimensional compounds can also exhibit a Zeeman-type spin splitting, allowing the splitting control by changing the growth direction of slabs formed by these compounds. We determine the required conditions for this effect: (i) noncentrosymmetric including polar and nonpolar point groups, (ii) valence band maximum or conduction band minimum in a generic k -point, i.e., non-time-reversal-invariant momentum, and (iii) zero magnetic moment. Using these conditions as filters, we perform a material screening to systematically search for these systems in the AFLOW-ICSD database. We find 20 candidates featuring the Zeeman-type effect. We also find that the spin splitting in confined systems can be controlled by an external electric field, which in turns can induce a metal–insulator transition. We believe that this work will open the way for the discovery of novel fundamental effects related to the spin polarization control. Zeeman effect ; Condensed matter physics ; Polar ; Spin (aerodynamics) ; Zero field splitting ; Polarization (electrochemistry) ; Physics ; Chemistry ; Spin polarization ; Magnetic field ; Electron ; Materials science ; Quantum mechanics 2D Materials and Applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Eric R. Homer | Derek M. Hensley | Conrad W. Rosenbrock | Andrew H. Nguyen | Gus L. W. Hart
Source: Frontiers in Materials (article, 2019-07-16)
Field: Materials Science — Materials Chemistry

Machine-Learning Informed Representations for Grain Boundary Structures The atomic structure of grain boundaries plays a defining but poorly understood role in the properties they exhibit. Due to the complex nature of these structures, machine learning is a natural tool for extracting meaningful relationships and new physical insight. We apply a new structural representation, called the scattering transform, that uses wavelet-based convolutional neural networks to characterize the complete three-dimensional atomic structure of a grain boundary. The machine learning to predict GB energy, mobility, and shear coupling using the scattering transform representation is compared and contrasted with learning using a smooth overlap of atomic positions (SOAP) based representation. While predictions using the scattering transform are not as good as those of SOAP, other factors suggest that the scattering transform may yet play an important role in GB structure learning. These factors include the ability of the scattering transform to learn well on larger datasets, in a process similar to deep learning, as well as their ability to provide physically interpretable information about what aspects of the GB structure contribute to the learning through an inverse scattering transform. Scattering ; Artificial intelligence ; Representation (politics) ; Computer science ; Wavelet transform ; Machine learning ; Physics ; Wavelet ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ilaria Rea | Luca De Stefano
Source: Sensors (review, 2019-11-28)
Field: Materials Science — Biomaterials

Recent Advances on Diatom-Based Biosensors Porous materials showing some useful transducing features, i.e., any changes in their physical or chemical properties as a consequence of molecular interaction, are very attractive in the realization of sensors and biosensors. Diatom frustules have been gaining support for biosensors since they are made of nanostructured amorphous silica, but do not require any nano-fabrication step; their surface can be easily functionalized and customized for specific application; diatom frustules are photoluminescent, and they can be found in almost every pond of water on the Earth, thus assuring large and low-cost availability. In this review, the most recent advances in diatom-based biosensors are reported, and a perspective view on future developments is given. Diatom ; Biosensor ; Nanotechnology ; Computer science ; Materials science ; Ecology ; Biology Diatoms and Algae Research ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Nadia Baskali-Bouregaa | Marie-Laure Milliand | Sophie Mauffrey | Elodie Chabert | Maureen Forrestier | Nicole Gilon
Source: Talanta (article, 2019-12-28)
Field: Chemistry — Analytical Chemistry

Tea geographical origin explained by LIBS elemental profile combined to isotopic information Principal component analysis ; Linear discriminant analysis ; Chemistry ; Sample (material) ; Partial least squares regression ; Analytical Chemistry (journal) ; Elemental analysis ; Chemometrics ; Pattern recognition (psychology) ; Statistics ; Chromatography ; Artificial intelligence ; Mathematics Heavy Metals in Plants ; Isotope Analysis in Ecology ; Cultural Heritage Materials Analysis
Authors: Tuo He | João Marco | Richard Soares | Yafang Yin | Alex C. Wiedenhoeft
Source: Forests (article, 2019-12-25)
Field: Chemistry — Organic Chemistry

Machine Learning Models with Quantitative Wood Anatomy Data Can Discriminate between Swietenia macrophylla and Swietenia mahagoni Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers—Decision Tree C5.0, Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers. Swietenia macrophylla ; Biology ; CITES ; Botany ; Artificial intelligence ; Computer science ; Ecology Wood and Agarwood Research ; Biological Stains and Phytochemicals ; Plant Diversity and Evolution