jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chia‐Hao Lee | Chuqiao Shi | Di Luo | Abid Khan | Blanka Janicek | Sangmin Kang | Wenjuan Zhu | Bryan K. Clark | Pinshane Y. Huang
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Deep Learning Enabled Measurements of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-Picometer Precision Journal Article Deep Learning Enabled Measurements of Single-Atom Defects in 2D Transition Metal Dichalcogenides with Sub-Picometer Precision Get access Chia-Hao Lee, Chia-Hao Lee Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Chuqiao Shi, Chuqiao Shi Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Di Luo, Di Luo Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Abid Khan, Abid Khan Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Blanka E Janicek, Blanka E Janicek Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Sangmin Kang, Sangmin Kang Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Wenjuan Zhu, Wenjuan Zhu Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Bryan K Clark, Bryan K Clark Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA Search for other works by this author on: Oxford Academic Google Scholar Pinshane Y Huang Pinshane Y Huang Department of Materials Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA Corresponding author: pyhuang@illinois.edu Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 172–173, https://doi.org/10.1017/S1431927619001594 Published: 01 August 2019 Library science ; Engineering physics ; Engineering ; Computer science Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Adelina Ion | Steluța Gosav | Mirela Praisler
Source:  (article, 2019-11-01)
Field: Chemistry — Spectroscopy

Screening for NBOMe Hallucinogens based on Artificial Neural Networks and Structural Descriptors Synthetic hallucinogen trafficking is a global illicit trade. It represents one of the main dangers to public health worldwide. NBOMe is a new class of synthetic hallucinogenic drugs of abuse, which is sold through illicit channels as alternative to LSD. The most representative member of the NBOMe class is 25I-NBOMe, a derivative of 2,5-dimethoxy-4-iodophenetylamine (2C-I). In this study we are presenting and comparing a series of Artificial Neural Networks (ANNs) designed to identify NBOMe hallucinogens based on their structural descriptors. Such a system may automatically predict the potential toxicity of new NBOMe compounds and thus saves analytical time and reduces the cost of toxicity studies. For this purpose, constitutional descriptors and functional groups of the optimized molecular structures of the main NBOMe hallucinogens have been determined. Then ANNs have been built by using only those descriptors found to be the most important structural descriptors. The efficiency of the ANNS was compared and the impact of variable selection on ANN performance was analyzed in detail based on several merit figures. Hallucinogen ; Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Pharmacology ; Medicine Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Sudaraka Mallawaarachchi | Yiyi Liu | San H. Thang | Wenlong Cheng | Malin Premaratne
Source: Physical Chemistry Chemical Physics (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning based temperature prediction of poly(<i>N</i>-isopropylacrylamide)-capped plasmonic nanoparticle solutions The temperature-dependent optical properties of gold nanoparticles that are capped with the thermo-sensitive polymer: 'poly(N-isopropylacrylamide)' (PNIPAM), have been studied extensively for several years. Also, their suitability to function as nanoscopic thermometers for bio-sensing applications has been suggested numerous times. In an attempt to establish this, many have studied the temperature-dependent optical resonance characteristics of these particles; however, developing a simple mathematical relationship between the optical measurements and the solution temperature remains an open challenge. In this paper, we attempt to systematically address this problem using machine learning techniques to quickly and accurately predict the solution-temperature, based on spectroscopic data. Our emphasis is on establishing a simple and practically useful solution to this problem. Our dataset comprises spectroscopic absorption data from both nanorods and nanobipyramids capped with PNIPAM, measured at discretely varied and pre-set temperature states. Specific regions of the spectroscopic data are selected as features for prediction using random forest (RF), gradient boosting (GB) and adaptive boosting (AB) regression techniques. Our prediction results indicate that RF and GB techniques can be used successfully to predict solution temperatures instantly to within 1 °C of accuracy. Materials science ; Nanoparticle ; Plasmon ; Poly(N-isopropylacrylamide) ; Boosting (machine learning) ; Surface plasmon resonance ; Nanoscopic scale ; Polymer ; Nanotechnology ; Computer science ; Biological system ; Machine learning ; Optoelectronics ; Copolymer Gold and Silver Nanoparticles Synthesis and Applications ; Free Radicals and Antioxidants ; Photochromic and Fluorescence Chemistry
Authors: Alexander Kovacs | Johann Fischbacher | Markus Gusenbauer | Harald Oezelt | Heike C. Herper | Olga Yu. Vekilova | P. Nieves | S. Arapan | T. Schrefl
Source: arXiv (Cornell University) (preprint, 2019-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Computational Design of the Rare-Earth Reduced Permanent Magnets <strong>ABSTRACT</strong> Multiscale simulation is a key research tool for the quest for new permanent magnets. Starting with first principles methods, a sequence of simulation methods can be applied to calculate the maximum possible coercive field and expected energy density product of a magnet made from a novel magnetic material composition. Fe-rich magnetic phases suitable for permanent magnets can be found by adaptive genetic algorithms. The intrinsic properties computed by ab initio simulations are used as input for micromagnetic simulations of the hysteresis properties of permanent magnets with realistic structure. Using machine learning techniques, the magnet’s structure can be optimized so that the upper limits for coercivity and energy density product for a given phase can be estimated. Structure property relations of synthetic permanent magnets were computed for several candidate hard magnetic phases. The following pairs (coercive field (T), energy density product (kJ/m³)) were obtained for Fe<sub>3</sub>Sn<sub>0.75</sub>Sb<sub>0.25</sub>: (0.49, 290), L1<sub>0</sub> FeNi: (1, 400), CoFe<sub>6</sub>Ta: (0.87, 425), and MnAl: (0.53, 80). Magnet ; Coercivity ; Materials science ; Condensed matter physics ; Hysteresis ; Micromagnetics ; Ab initio ; Energy (signal processing) ; Magnetic field ; Mechanical engineering ; Computational physics ; Physics ; Magnetization ; Engineering Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Mitsuyoshi Onoda
Source: IEEJ Transactions on Electrical and Electronic Engineering (article, 2019-12-02)
Field: Materials Science — Polymers and Plastics

Bioinspired electrochemical devices toward organic iontronics Currently, a paradigm shift of technology from ‘hard and dry’ to ‘soft and wet’ is taking place, that is, the industrial importance is moving from heavy materials and silicon semiconductors to artificial intelligence (AI) and life science. Electronic materials of hard and dry technologies are silicon and inorganic semiconductors; electrons are playing the role of functions. Of course, the materials for soft and wet are organics, and ions are working with electrons. In other words, the recent progress witnessed by ground‐breaking research in the robotics by AI and tissue engineering by induced pluripotent stem (iPS) cell made it hard to anticipate about the future social life. However, food, life‐sustaining biomaterials (hormone, virus, enzyme, etc.), and energy, which are essential for life, are constantly being improved based on existing materials and technology. Biologically active organic compounds control various life functions; therefore, a fusion of biomaterials, energy, and informatics are expected to provide an efficient novel system for our sustainable growth and development. Biological functions can be imitated by dynamic control of mass and ion transfer, including conventional electronics. The author is sure that this review article will provide key technologies in the future to solve the issues on environmental protection and energy problems for the life sustainability and the need to be organized successively. The main purpose of this review article is to provide an opportunity for researchers who are interested in organic ‘iontronics’, to come together in an informal and friendly atmosphere and exchange their technical information and experiences. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc. Nanotechnology ; Electronics ; Sustainability ; Computer science ; Systems engineering ; Engineering management ; Engineering ; Electrical engineering ; Materials science ; Ecology Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Analytical Chemistry and Sensors
Authors: Oleksandr I. Malyi | Gustavo M. Dalpian | Xingang Zhao | Zhi Wang | Alex Zunger
Source: Materials Today (preprint, 2019-09-25)
Field: Materials Science — Materials Chemistry

Realization of predicted exotic materials: The burden of proof Realization (probability) ; Symmetry (geometry) ; Theoretical physics ; Instability ; Symmetry breaking ; Physics ; Proof of concept ; Statistical physics ; Topology (electrical circuits) ; Chemical physics ; Computer science ; Quantum mechanics ; Mathematics ; Geometry ; Combinatorics Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics ; Magnetic and transport properties of perovskites and related materials
Authors: Saeed Babaee | Seyed Ghorban Hosseini | Mohammad Mirzaei
Source: Eclética Química (article, 2019-10-01)
Field: Chemistry — Analytical Chemistry

Rapid and selective extraction of trace amount of Pb(II) in aqueous samples using a magnetic ion-imprinted polymer and detection by flame atomic absorption spectrometry A magnetic Pb(II) ion-imprinted polymer was synthesized using magnetic Fe3O4@SiO2 nanospheres as supporter, n-benzoyl-n-phenyl hydroxylamine as ligand, 4-vinyl pyridine as monomer, ethylene glycol dimethacrylate as crosslinker and 2,2′-azobis(isobutyronitrile) as the initiator. The template was removed from the polymer using 4 mL of HCl (2 mol L-1). The chemical structure, morphology-particle size, elemental analysis, thermal behavior and magnetic properties of the sorbent were evaluated using Fourier-transform infrared spectroscopy, scanning electron microscopy &amp; ImageJ software - Energy dispersive X-ray system, thermal gravimetric analysis and vibrating sample magnetometry. Various parameters such as pH, equilibrium extraction time, temperature and the eluent were optimized. The results showed the sorbent adsorption capacity was 92.3 mg g-1 in pH 6.0 media, equilibrium time of 7 min at 40 °C. Calibration linearity was 2-150 µg L-1 with detection limit of 0.3 µg L-1. The prepared sorbent had high selectivity and was successfully applied to the removing of lead in real samples. Sorbent ; Detection limit ; Analytical Chemistry (journal) ; Ethylene glycol dimethacrylate ; Chemistry ; Extraction (chemistry) ; Adsorption ; Aqueous solution ; Fourier transform infrared spectroscopy ; Thermogravimetric analysis ; Nuclear chemistry ; Polymer ; Materials science ; Monomer ; Chromatography ; Chemical engineering ; Methacrylic acid ; Physical chemistry ; Organic chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Radioactive element chemistry and processing
Authors: Kai Li | Xiao Li | Jian Li | Jiamiao Xie
Source: Journal of Inorganic Materials (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Structural Stability of Ni-Fe Supported Solid Oxide Fuel Cells Based on Stress Analysis Ni-Fe Materials science ; Oxide ; Stress (linguistics) ; Chemical engineering ; Fuel cells ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials
Authors: Akshay Iyer | Yichi Zhang | Aditya Prasad | Siyu Tao | Yixing Wang | Linda S. Schadler | L. Catherine Brinson | Wei Chen
Source: arXiv (Cornell University) (preprint, 2019-07-04)
Field: Materials Science — Materials Chemistry

Data-Centric Mixed-Variable Bayesian Optimization For Materials Design Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify "lack of data" uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems. Bayesian optimization ; Computer science ; Pareto principle ; Mathematical optimization ; Gaussian process ; Optimal design ; Bayesian probability ; Variable (mathematics) ; Surrogate model ; Latent variable ; Gaussian ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Computational Drug Discovery Methods
Authors: Joseph F. Rudzinski
Source: Computation (preprint, 2019-08-20)
Field: Chemistry — Physical and Theoretical Chemistry

Recent Progress towards Chemically-Specific Coarse-Grained Simulation Models with Consistent Dynamical Properties Coarse-grained (CG) models can provide computationally efficient and conceptually simple characterizations of soft matter systems. While generic models probe the underlying physics governing an entire family of free-energy landscapes, bottom-up CG models are systematically constructed from a higher-resolution model to retain a high level of chemical specificity. The removal of degrees of freedom from the system modifies the relationship between the relative time scales of distinct dynamical processes through both a loss of friction and a “smoothing” of the free-energy landscape. While these effects typically result in faster dynamics, decreasing the computational expense of the model, they also obscure the connection to the true dynamics of the system. The lack of consistent dynamics is a serious limitation for CG models, which not only prevents quantitatively accurate predictions of dynamical observables but can also lead to qualitatively incorrect descriptions of the characteristic dynamical processes. With many methods available for optimizing the structural and thermodynamic properties of chemically-specific CG models, recent years have seen a stark increase in investigations addressing the accurate description of dynamical properties generated from CG simulations. In this review, we present an overview of these efforts, ranging from bottom-up parameterizations of generalized Langevin equations to refinements of the CG force field based on a Markov state modeling framework. We aim to make connections between seemingly disparate approaches, while laying out some of the major challenges as well as potential directions for future efforts. Statistical physics ; Observable ; Dynamical systems theory ; Computer science ; Smoothing ; Degrees of freedom (physics and chemistry) ; Simple (philosophy) ; Langevin dynamics ; Field (mathematics) ; Physics ; Mathematics Advanced Physical and Chemical Molecular Interactions ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Wellington de Oliveira | Pedro Luíz de Paula Filho | Jefferson Gustavo Martins
Source: FLORESTA (article, 2019-07-16)
Field: Chemistry — Organic Chemistry

SOFTWARE FOR FOREST SPECIES RECOGNITION BASED ON DIGITAL IMAGES OF WOOD Classifying forest species is an essential process for the correct management of wood and forest control. After cutting off the trunk of the tree, many of the characteristics of the species are lost and identifying them becomes a much more difficult task. In this context, an anatomical analysis of the wood becomes necessary, needing to be done by specialists who know very well the cellular structures of each species. However, such methodology approaches few automated techniques, making it a delayed and error-prone activity. These factors undermine environmental control and decision-making. The use of computer vision is an alternative to automatic recognition, since it allows the development of intelligent systems, in which, from images, are able to detect features and perform a final classification. One of the techniques of Computer Vision is the use of Convolutional Neural Networks, technique that represents the state of the art in this area, it is the construction of models capable of interpreting patterns in images. This research addresses experiments using convolutional neural networks for recognizing forest species from digital images. Two original datasets were used, one including macroscopic images and the other including microscopic images, for which three models were created: scale recognition, species recognition from macroscopic images and species recognition from microscopic. The best models provide 100% recognition rates for the scale dataset, 98.73% for the macroscopic and 99.11% for the microscopic which made possible to develop a software as a final product, using these three models. Convolutional neural network ; Computer science ; Artificial intelligence ; Software ; Process (computing) ; Context (archaeology) ; Decision tree ; Scale (ratio) ; Pattern recognition (psychology) ; Tree (set theory) ; Machine learning ; Computer vision ; Geography ; Cartography ; Mathematics Wood and Agarwood Research
Authors: Hanane Essebaai | I. Ismi | Ahmed Lebkiri | S. Marzak | El Housseine Rifi
Source: Mediterranean Journal of Chemistry (article, 2019-09-13)
Field: Materials Science — Biomaterials

Kinetic and Thermodynamic Study of Adsorption of Copper (II) Ion on Moroccan Clay Highly efficient low-cost adsorbent was applied for copper (II) ions uptake from aqueous solution. Characteristics of natural adsorbent were established using scanning X-ray diffraction (XRD), X-ray fluorescence, electron microscope (SEM) and Fourier Transform Infra-Red (FTIR). Various physicochemical parameters such as contact time, initial copper(II) ions concentration, adsorbent dosage, pH of copper (II) ions solution and temperature were investigated. The result showed that the adsorption of copper (II) ions by natural clay was favorable at pH=5,5. The adsorption was found to increase with increase in initial copper (II) ions concentration, and contact time. Equilibrium adsorption data were fitted using three isotherms and kinetic data tested with four kinetic models. Freundlich isotherm best described the adsorption of copper (II) ions onto utilised clay, the maximum monolayer adsorption capacity (qmax) was 8 mg/g. Pseudo-second-order model best described the kinetics of the adsorption process. Thermodynamic parameters such as Gibbs free energy, enthalpy and entropy were determined. It was found that copper (II) ions adsorption was spontaneous (ΔG°&lt;0) and endothermic (ΔH°&gt;0). Chemistry ; Adsorption ; Copper ; Endothermic process ; Enthalpy ; Gibbs free energy ; Freundlich equation ; Ion ; Aqueous solution ; Fourier transform infrared spectroscopy ; Inorganic chemistry ; Monolayer ; Nuclear chemistry ; Physical chemistry ; Thermodynamics ; Chemical engineering ; Organic chemistry Clay minerals and soil interactions ; Adsorption and biosorption for pollutant removal ; Iron oxide chemistry and applications
Authors: Zhichao Sun | Long Huang | Jing Cao | Zhikun Yin
Source: Materials Research (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Tri-modal Microstructure in Different Loading Zones Under TA15 Ti-alloy Isothermal Local Conventional Forging and Given Subsequent Heat Treatment For TA15 Ti-alloy large-scale component, isothermal local conventional forging (ILCF) combined with subsequent heat treatment provides a feasible approach to obtain a tri-modal microstructure with attractive comprehensive performance. However the tri-modal microstructure characteristics and evolution laws in the first and second loading zones are lack of understanding. In this paper, the effects of ILCF conditions on the tri-modal microstructure in the first and second loading zones were investigated under given subsequent heat treatment and the reasonable ILCF conditions were proposed. As forging temperature increases, in both loading zones the content and aspect of equiaxed αp decrease obviously while the content and thickness of lamellar αs increase. The content and thickness of lamellar αs in the second loading zone are higher. Forged at a moderate strain rate of 0.1s-1, the content of lamellar αs is higher, and the differences in the content and morphology of constituent phases in both loading zones are relatively small. Compared with air cooling after forging, water quenching result in more and disordered lamellar αs, and less differences in obtained tri-modal microstructure in the first and second loading zones. Under three loading passes, the uniformity of tri-modal microstructure in the first and second loading zones is better. Microstructure ; Lamellar structure ; Materials science ; Equiaxed crystals ; Isothermal process ; Forging ; Alloy ; Metallurgy ; Composite material ; Quenching (fluorescence) ; Thermodynamics Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Microstructure and mechanical properties