jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniele Torsello | K. Cho | Kamal R. Joshi | Sunil Ghimire | G. A. Ummarino | Naufer Nusran | M. A. Tanatar | William R. Meier | Mingyu Xu | S.L. Bud’ko | P. C. Canfield | G. Ghigo | R. Prozorov
Source: Physical review. B./Physical review. B (article, 2019-09-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Analysis of the London penetration depth in Ni-doped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>CaKFe</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:msub><mml:mi>As</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math> We report combined experimental and theoretical analysis of superconductivity in $\mathrm{CaK}{({\mathrm{Fe}}_{1\ensuremath{-}x}{\mathrm{Ni}}_{x})}_{4}{\mathrm{As}}_{4}$ (CaK1144) for $x=0$, 0.017, and 0.034. To obtain the superfluid density $\ensuremath{\rho}={\left[1+\mathrm{\ensuremath{\Delta}}{\ensuremath{\lambda}}_{L}(T)/{\ensuremath{\lambda}}_{L}(0)\right]}^{\ensuremath{-}2}$, the temperature dependence of the London penetration depth $\mathrm{\ensuremath{\Delta}}{\ensuremath{\lambda}}_{L}(T)$ was measured by using a tunnel-diode resonator (TDR) and the results agreed with the microwave coplanar resonator (MWR) with the small differences accounted for by considering a three orders of magnitude higher frequency of MWR. The absolute value of ${\ensuremath{\lambda}}_{L}(T\ensuremath{\ll}{T}_{c})\ensuremath{\approx}{\ensuremath{\lambda}}_{L}(0)$ was measured by using MWR, ${\ensuremath{\lambda}}_{L}(5\phantom{\rule{4pt}{0ex}}\mathrm{K})\ensuremath{\approx}170\ifmmode\pm\else\textpm\fi{}20$ nm, which agreed well with the NV centers in diamond optical magnetometry that gave ${\ensuremath{\lambda}}_{L}(5\phantom{\rule{4pt}{0ex}}\mathrm{K})\ensuremath{\approx}196\ifmmode\pm\else\textpm\fi{}12$ nm. The experimental results are analyzed within the Eliashberg theory, showing that the superconductivity of CaK1144 is well described by the nodeless ${s}_{\ifmmode\pm\else\textpm\fi{}}$ order parameter and that upon Ni doping the interband interaction increases. Doping ; Physics ; Condensed matter physics Iron-based superconductors research ; Rare-earth and actinide compounds ; Superconductivity in MgB2 and Alloys
Authors: Zhiyong Bao | Nianqing Fu | Yongqiang Qin | Jun Lv | Yan Wang | Jijun He | Yidong Hou | Chenyi Jiao | Dongchu Chen | Yucheng Wu | Jiyan Dai
Source: ACS Applied Materials & Interfaces (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

Broadband Plasmonic Enhancement of High-Efficiency Dye-Sensitized Solar Cells by Incorporating Au@Ag@SiO<sub>2</sub> Core–Shell Nanocuboids The introduction of plasmonic additives is a promising approach to boost the efficiency of the dye-sensitized solar cell (DSSC) since they may improve the light harvesting of a solar cell. Herein, we design broadband and strong plasmonic absorption Au@Ag@SiO<sub>2</sub> nanocuboids (GSS NCs) as nanophotonic inclusions to achieve plasmon-enhanced DSSCs. These multiple-resonance absorptions arising from GSS NCs can be readily adjusted by altering their structures to complementarily match the absorption spectra of the dyes, especially in weak absorption zones. By subtly regulating the position of nanophotonic inclusions in the photoanodes, not only the plasmonic near-field enhancement but also far-field light scattering could be adequately developed to promote the light harvest and thus the efficiency of DSSCs. The resulting solar cells yield an average efficiency of 10.34%, with a champion value of 10.58%. The electromagnetic simulations are consistent with the experimental observations, further corroborating the synergistic effect of plasmonic improvement in these DSSCs. Plasmon ; Dye-sensitized solar cell ; Materials science ; Absorption (acoustics) ; Optoelectronics ; Nanophotonics ; Plasmonic solar cell ; Surface plasmon resonance ; Solar cell ; Nanotechnology ; Nanoparticle ; Polymer solar cell ; Physics Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Perovskite Materials and Applications
Authors: Mehmet Özdoğan | Cem Çelebi | G. Utlu
Source: Materials Science in Semiconductor Processing (article, 2019-11-26)
Field: Materials Science — Materials Chemistry

Mechanisms behind slow photoresponse character of Pulsed Electron Deposited ZnO thin films Materials science ; Character (mathematics) ; Thin film ; Electron ; Optoelectronics ; Nanotechnology ; Engineering physics ZnO doping and properties ; Chalcogenide Semiconductor Thin Films ; Copper-based nanomaterials and applications
Authors: Youyuan Wang | Yudong Li | Zhanxi Zhang | Yànfāng Zhāng
Source: Applied Sciences (article, 2019-07-27)
Field: Materials Science — Materials Chemistry

Effect of Doping Microcapsules on Typical Electrical Performances of Self-Healing Polyethylene Insulating Composite Polyethylene cables, as important transmission equipment of modern power grid, would inevitably be slightly damaged, which seriously threatens the safety of the power supply. This paper has pioneered the preparation and typical performances of a self-healing polyethylene insulating composite. The self-healing performance to structural damage was verified by tests of electrical and mechanical damage. The effect mechanism of doping microcapsules on the electrical performance of polyethylene was emphatically analyzed. The results show that in appropriate conditions (such as 60 °C/30 min), the composite can not only repair the electrical tree and scratches, but also restore the insulation strength of damaged area. The effect of doping microcapsules on the electrical performances of polyethylene, such as breakdown strength, volumetric resistivity, dielectric properties, and space charge characteristics, are mainly related to impurity and the interface of microcapsule. Polarization and ionization of impurities can reduce the electrical performance of polyethylene. The interface not only improves the microstructure of polyethylene (such as how the heterogeneous nucleation effect increases the number of crystal regions, and the anchoring effect enhances the stability of amorphous regions), but also increases the charge traps. Moreover, the microstructure and charge trap can affect the characteristics of carrier transport, material polarization, and space charge accumulation, thus improving the electrical performance of polyethylene. In addition, the important electrical performance of the composite can meet the basic application requirements of polyethylene insulating material, which has good application prospects. Polyethylene ; Materials science ; Composite material ; Microstructure ; Space charge ; Composite number ; Doping ; Dielectric ; Optoelectronics High voltage insulation and dielectric phenomena ; Polymer composites and self-healing ; Surface Modification and Superhydrophobicity
Authors: Martin Hangaard Hansen | José Antonio Garrido Torres | Paul C. Jennings | Ziyun Wang | Jacob R. Boes | Osman Mamun | Thomas Bligaard
Source: arXiv (Cornell University) (preprint, 2019-04-01)
Field: Materials Science — Materials Chemistry

An Atomistic Machine Learning Package for Surface Science and Catalysis We present work flows and a software module for machine learning model building in surface science and heterogeneous catalysis. This includes fingerprinting atomic structures from 3D structure and/or connectivity information, it includes descriptor selection methods and benchmarks, and it includes active learning frameworks for atomic structure optimization, acceleration of screening studies and for exploration of the structure space of nano particles, which are all atomic structure problems relevant for surface science and heterogeneous catalysis. Our overall goal is to provide a repository to ease machine learning model building for catalysis, to advance the models beyond the chemical intuition of the user and to increase autonomy for exploration of chemical space. Intuition ; Computer science ; Autonomy ; Surface (topology) ; Chemical space ; Nanotechnology ; Artificial intelligence ; Materials science ; Chemistry ; Cognitive science Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Ralf Meyer | Klemens Schmuck | Andreas Hauser
Source: Journal of Chemical Theory and Computation (article, 2019-09-25)
Field: Materials Science — Materials Chemistry

Machine Learning in Computational Chemistry: An Evaluation of Method Performance for Nudged Elastic Band Calculations The localization of transition states and the calculation of reaction pathways are routine tasks of computational chemists but often very CPU-intense problems, in particular for large systems. The standard algorithm for this purpose is the nudged elastic band method, but it has become obvious that an "intelligent" selection of points to be evaluated on the potential energy surface can improve its convergence significantly. This article summarizes, compares, and extends known strategies that have been heavily inspired by the machine learning developments of recent years. It presents advantages and disadvantages and provides an unbiased comparison of neural network based approaches, Gaussian process regression in Cartesian coordinates, and Gaussian approximation potentials. We test their performance on two example reactions, the ethane rotation and the activation of carbon dioxide on a metal catalyst, and provide a clear ranking in terms of usability for future implementations. Computer science ; Kriging ; Implementation ; Gaussian process ; Ranking (information retrieval) ; Usability ; Convergence (economics) ; Potential energy surface ; Gaussian ; Artificial neural network ; Machine learning ; Artificial intelligence ; Algorithm ; Computational chemistry ; Chemistry ; Ab initio ; Human–computer interaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Gabriele C. Sosso | Marco Bernasconi
Source: MRS Bulletin (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Harnessing machine learning potentials to understand the functional properties of phase-change materials Phase change ; Materials science ; Phase (matter) ; Nanotechnology ; Computer science ; Cognitive science ; Psychology ; Engineering physics ; Engineering ; Chemistry Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Evan Miller | Matthew L. Jones | Michael M. Henry | Bryan Stanfill | Eric Jankowski
Source: AIChE Journal (article, 2019-08-19)
Field: Materials Science — Materials Chemistry

Machine learning predictions of electronic couplings for charge transport calculations of P3HT Abstract The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly‐(3‐hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information‐dense, and easy‐to‐implement approach for this problem, achieving mean‐absolute‐error of 0.02 [× 1.6 × 10 −19 J], R 2 = 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, and informing zero‐field hole mobilities within 5% of prior work. We discuss strategies for identifying small effective training sets. In sum, we demonstrate an example problem where machine learning techniques provide an effective reduction in computational costs while helping to understand underlying structure–property relationships in a materials system with broad applicability. Charge (physics) ; Computer science ; Work (physics) ; Field (mathematics) ; Quantum ; Organic semiconductor ; Reduction (mathematics) ; Statistical physics ; Quantum chemical ; Machine learning ; Artificial intelligence ; Chemistry ; Physics ; Mathematics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Ruirui Wang | Shuming Zeng | Xinming Wang | Jun Ni
Source: Computational Materials Science (article, 2019-05-08)
Field: Materials Science — Materials Chemistry

Machine learning for hierarchical prediction of elastic properties in Fe-Cr-Al system Shear modulus ; Bulk modulus ; Ternary operation ; Materials science ; Elastic modulus ; Modulus ; Artificial neural network ; Young's modulus ; Thermodynamics ; Composite material ; Computer science ; Physics ; Machine learning Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Alexander S. Gzyl | Anton O. Oliynyk | Lawrence A. Adutwum | Arthur Mar
Source: Inorganic Chemistry (article, 2019-06-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solving the Coloring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation The site preferences within the structures of half-Heusler compounds have been evaluated through a machine-learning approach. A support-vector machine algorithm was applied to develop a model which was trained on 179 experimentally reported structures and 23 descriptors based solely on the chemical composition. The model gave excellent performance, with sensitivity of 93%, selectivity of 96%, and accuracy of 95%. As an illustration of data sanitization, two compounds (GdPtSb, HoPdBi) flagged by the model to have potentially incorrect site assignments were resynthesized and structurally characterized. The predictions of the correct site assignments from the machine-learning model were confirmed by single-crystal and powder X-ray diffraction analysis. These site assignments also corresponded to the lowest total energy configurations as revealed from first-principles calculations. Chemistry ; Support vector machine ; Selectivity ; Sensitivity (control systems) ; Crystal structure ; Diffraction ; Artificial intelligence ; Experimental data ; Machine learning ; Crystallography ; Optics ; Computer science ; Statistics ; Physics ; Catalysis ; Organic chemistry ; Engineering Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Yuanchao Yang | Dezhen Xue | Ruihao Yuan | Yumei Zhou | Turab Lookman | Xiangdong Ding | Xiaobing Ren | Jun Sun
Source: Acta Materialia (article, 2019-06-21)
Field: Materials Science — Materials Chemistry

Doping effects of point defects in shape memory alloys Materials science ; Dopant ; Pseudoelasticity ; Shape-memory alloy ; Doping ; Condensed matter physics ; Diffusionless transformation ; Transformation (genetics) ; Stress (linguistics) ; Deformation (meteorology) ; Martensite ; Thermodynamics ; Composite material ; Microstructure ; Optoelectronics ; Physics ; Chemistry Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Advanced Memory and Neural Computing
Authors: Önder Ayer
Source: Transactions of Nonferrous Metals Society of China (article, 2019-04-01)
Field: Materials Science — Biomaterials

A forming load analysis for extrusion process of AZ31 magnesium Extrusion ; Materials science ; Magnesium alloy ; Finite element method ; Die (integrated circuit) ; Metallurgy ; Forming processes ; Composite material ; Alloy ; Structural engineering ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Jing Zhou | Yingguang Li | Di Li | Youyi Wen
Source: Chemical Engineering Journal (article, 2019-03-23)
Field: Chemistry — Organic Chemistry

Online learning based intelligent temperature control during polymer composites microwave curing process Curing (chemistry) ; Materials science ; Microwave ; Composite material ; Composite number ; Superposition principle ; Microwave cavity ; Aerospace ; Polymer ; Temperature control ; Composite laminates ; Process engineering ; Mechanical engineering ; Computer science ; Engineering ; Aerospace engineering Microwave-Assisted Synthesis and Applications ; Epoxy Resin Curing Processes ; Nanofabrication and Lithography Techniques
Authors: Hiroshi Ohno
Source: Applied Soft Computing (article, 2019-11-13)
Field: Materials Science — Materials Chemistry

Training data augmentation: An empirical study using generative adversarial net-based approach with normalizing flow models for materials informatics Computer science ; Benchmark (surveying) ; Machine learning ; Artificial intelligence ; Autoregressive model ; Generative grammar ; Estimator ; Regression ; Artificial neural network ; Generator (circuit theory) ; Kriging ; Data mining ; Statistics ; Mathematics Machine Learning in Materials Science ; Neural Networks and Applications ; Generative Adversarial Networks and Image Synthesis
Authors: Jing Zhao | Pengdi Cui | Huan Liu | Chunhua Wang | Ming Liu | Gang Li | Mingzhong Pan | Zheng Li | Tongchuan Suo
Source: Infrared Physics & Technology (article, 2019-11-23)
Field: Chemistry — Analytical Chemistry

Rapid screening and quantitative analysis of adulterant Lonicerae Flos in Lonicerae Japonicae Flos by Fourier-transform near infrared spectroscopy Adulterant ; Flos ; Chemometrics ; Partial least squares regression ; Principal component analysis ; Chromatography ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Chemistry ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Christopher Morris | Gaurav Rattan | Petra Mutzel
Source: arXiv (Cornell University) (preprint, 2019-04-02)
Field: Materials Science — Materials Chemistry

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings Graph kernels based on the $1$-dimensional Weisfeiler-Leman algorithm and corresponding neural architectures recently emerged as powerful tools for (supervised) learning with graphs. However, due to the purely local nature of the algorithms, they might miss essential patterns in the given data and can only handle binary relations. The $k$-dimensional Weisfeiler-Leman algorithm addresses this by considering $k$-tuples, defined over the set of vertices, and defines a suitable notion of adjacency between these vertex tuples. Hence, it accounts for the higher-order interactions between vertices. However, it does not scale and may suffer from overfitting when used in a machine learning setting. Hence, it remains an important open problem to design WL-based graph learning methods that are simultaneously expressive, scalable, and non-overfitting. Here, we propose local variants and corresponding neural architectures, which consider a subset of the original neighborhood, making them more scalable, and less prone to overfitting. The expressive power of (one of) our algorithms is strictly higher than the original algorithm, in terms of ability to distinguish non-isomorphic graphs. Our experimental study confirms that the local algorithms, both kernel and neural architectures, lead to vastly reduced computation times, and prevent overfitting. The kernel version establishes a new state-of-the-art for graph classification on a wide range of benchmark datasets, while the neural version shows promising performance on large-scale molecular regression tasks. Overfitting ; Computer science ; Scalability ; Artificial intelligence ; Theoretical computer science ; Machine learning ; Kernel (algebra) ; Graph ; Tuple ; Expressive power ; Graph kernel ; Artificial neural network ; Kernel method ; Mathematics ; Support vector machine ; Polynomial kernel ; Discrete mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Wanisa Abdussalam-Mohammed
Source: Journal of Chemical Reviews (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Review of Therapeutic Applications of Nanotechnology in Medicine Field and its Side Effects Nanotechnology is the new field of technological innovation on their application in human biology and medicine. On the other hand, there is an important concern about the potential health and environmental risks, due to exposure to human body of nanoparticles. These nanoparticles led to the appearance in the scientific disciplines such as nanotoxicology and nanomedicine. Nanotoxicology is the study toxicity effects of nanoparticles on human health as well as environment. Nano-particles have been developed for beneficial studies with applications in medical field and devices. Also, nanomedicines have been applied to the sector of molecular biology, tissue culture, bio-materials, genetic engineering, bio and bio-imaging sensors. Actually, nanotechnologies have followed healthcare improvements including potentially revolutionary treatments for cancer, critical diseases and also drug improvements with reduction of side effect. With all these descriptions, the extent of potential impact on human health is still a major concern due to lack of reliable toxicity data of nanoparticles. The main purpose of present literature is providing of better understanding on nanobiotechnology principles and its applications in biomedicine have been discussed as well as to their side effects. Nanomedicine ; Biomedicine ; Nanotoxicology ; Nanotechnology ; Nanobiotechnology ; Human health ; Engineering ethics ; Risk analysis (engineering) ; Medicine ; Engineering ; Nanoparticle ; Materials science ; Biology ; Bioinformatics Nanoparticles: synthesis and applications ; Biotechnology and Related Fields ; Computational Drug Discovery Methods
Authors: Bruno Conti | Bárbara Bosio | Stephen J. McPhail | Francesca Santoni | Davide Pumiglia | Elisabetta Arato
Source: Catalysts (article, 2019-01-02)
Field: Materials Science — Materials Chemistry

A 2-D model for Intermediate Temperature Solid Oxide Fuel Cells Preliminarily Validated on Local Values Intermediate Temperature Solid Oxide Fuel Cell (IT-SOFC) technology offers interesting opportunities in the panorama of a larger penetration of renewable and distributed power generation, namely high electrical efficiency at manageable scales for both remote and industrial applications. In order to optimize the performance and the operating conditions of such a pre-commercial technology, an effective synergy between experimentation and simulation is fundamental. For this purpose, starting from the SIMFC (SIMulation of Fuel Cells) code set-up and successfully validated for Molten Carbonate Fuel Cells, a new version of the code has been developed for IT-SOFCs. The new release of the code allows the calculation of the maps of the main electrical, chemical, and physical parameters on the cell plane of planar IT-SOFCs fed in co-flow. A semi-empirical kinetic formulation has been set-up, identifying the related parameters thanks to a devoted series of experiments, and integrated in SIMFC. Thanks to a multi-sampling innovative experimental apparatus the simultaneous measurement of temperature and gas composition on the cell plane was possible, so that a preliminary validation of the model on local values was carried out. A good agreement between experimental and simulated data was achieved in terms of cell voltages and local temperatures, but also, for the first time, in terms of local concentration on the cell plane, encouraging further developments. This numerical tool is proposed for a better interpretation of the phenomena occurring in IT-SOFCs and a consequential optimization of their performance. Oxide ; Mechanical engineering ; Process engineering ; Fuel cells ; Nuclear engineering ; Computer science ; Solid oxide fuel cell ; Materials science ; Chemical engineering ; Engineering ; Chemistry ; Anode ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Chemical Looping and Thermochemical Processes