jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sunae So | Jungho Mun | Junsuk Rho
Source: ACS Applied Materials & Interfaces (article, 2019-06-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simultaneous Inverse Design of Materials and Structures via Deep Learning: Demonstration of Dipole Resonance Engineering Using Core–Shell Nanoparticles Recent introduction of data-driven approaches based on deep-learning technology has revolutionized the field of nanophotonics by allowing efficient inverse design methods. In this paper, a simultaneous inverse design of materials and structure parameters of core-shell nanoparticles is achieved for the first time using deep learning of a neural network. A neural network to learn the correlation between the extinction spectra of electric and magnetic dipoles and core-shell nanoparticle designs, which include material information and shell thicknesses, is developed and trained. We demonstrate deep-learning-assisted inverse design of core-shell nanoparticles for (1) spectral tuning electric dipole resonances, (2) finding spectrally isolated pure magnetic dipole resonances, and (3) finding spectrally overlapped electric dipole and magnetic dipole resonances. Our finding paves the way for the rapid development of nanophotonics by allowing a practical utilization of deep-learning technology for nanophotonic inverse design. Materials science ; Nanoparticle ; Dipole ; Core (optical fiber) ; Nanotechnology ; Shell (structure) ; Inverse ; Resonance (particle physics) ; Composite material ; Organic chemistry ; Atomic physics ; Geometry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Kailun Zou | Dan Yu | Haojie Xu | Qingfeng Zhang | Yinmei Lu | Haitao Huang | Yunbin He
Source: Materials Research Bulletin (article, 2019-02-05)
Field: Materials Science — Materials Chemistry

Recent advances in lead-free dielectric materials for energy storage Dielectric ; Materials science ; Capacitor ; Energy storage ; Polarization (electrochemistry) ; Engineering physics ; Ceramic ; Optoelectronics ; Electric field ; Composite material ; Voltage ; Electrical engineering ; Engineering Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Microwave Dielectric Ceramics Synthesis
Authors: Ngonidzashe Masunga | Olga K. Mmelesi | Kebede K. Kefeni | Bhekie B. Mamba
Source: Journal of environmental chemical engineering (article, 2019-05-28)
Field: Materials Science — Materials Chemistry

Recent advances in copper ferrite nanoparticles and nanocomposites synthesis, magnetic properties and application in water treatment: Review Nanocomposite ; Nanomaterials ; Materials science ; Ferrite (magnet) ; Photocatalysis ; Water treatment ; Portable water purification ; Reuse ; Nanoparticle ; Nanotechnology ; Calcination ; Magnetic nanoparticles ; Copper ; Dopant ; Chemical engineering ; Metallurgy ; Doping ; Waste management ; Chemistry ; Environmental engineering ; Environmental science ; Composite material ; Catalysis Magnetic Properties and Synthesis of Ferrites ; Copper-based nanomaterials and applications ; Multiferroics and related materials
Authors: Xuan Zhang | Linfeng Chen | Kang Hui Lim | Spandhana Gonuguntla | Kang Wen Lim | Dicky Pranantyo | Wai Pong Yong | Wei Jian Tyler Yam | Zhida Low | Wee Joon Teo | Hao Ping Nien | Qiao Wen Loh | Siowling Soh
Source: Advanced Materials (review, 2019-01-09)
Field: Materials Science — Biomaterials

The Pathway to Intelligence: Using Stimuli‐Responsive Materials as Building Blocks for Constructing Smart and Functional Systems Systems that are intelligent have the ability to sense their surroundings, analyze, and respond accordingly. In nature, many biological systems are considered intelligent (e.g., humans, animals, and cells). For man-made systems, artificial intelligence is achieved by massively sophisticated electronic machines (e.g., computers and robots operated by advanced algorithms). On the other hand, freestanding materials (i.e., not tethered to a power supply) are usually passive and static. Hence, herein, the question is asked: can materials be fabricated so that they are intelligent? One promising approach is to use stimuli-responsive materials; these "smart" materials use the energy supplied by a stimulus available from the surrounding for performing a corresponding action. After decades of research, many interesting stimuli-responsive materials that can sense and perform smart functions have been developed. Classes of functions discussed include practical functions (e.g., targeting and motion), regulatory functions (e.g., self-regulation and amplification), and analytical processing functions (e.g., memory and computing). The pathway toward creating truly intelligent materials can involve incorporating a combination of these different types of functions into a single integrated system by using stimuli-responsive materials as the basic building blocks. Materials science ; Smart material ; Nanotechnology ; Systems engineering ; Human–computer interaction ; Computer science ; Engineering Supramolecular Self-Assembly in Materials ; Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research
Authors: Kunal Ghosh | Annika Stuke | Milica Todorović | Peter Bjørn Jørgensen | Mikkel N. Schmidt | Aki Vehtari | Patrick Rinke
Source: Advanced Science (article, 2019-01-29)
Field: Materials Science — Materials Chemistry

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications. Spectroscopy ; Excitation ; Artificial neural network ; Spectral line ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Physics ; Astronomy Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Thomas M. Robinson | Dietmar W. Hutmacher | Paul D. Dalton
Source: Advanced Functional Materials (article, 2019-08-18)
Field: Materials Science — Biomaterials

The Next Frontier in Melt Electrospinning: Taming the Jet Abstract There is a specialized niche for the electrohydrodynamic jetting of melts, from biomedical products to filtration and soft matter applications. The next frontier includes optics, microfluidics, flexible electronic devices, and soft network composites in biomaterial science and soft robotics. The recent emphasis on reproducibly direct‐writing continual molten jets has enabled a spectrum of contemporary microscale 3D objects to be fabricated. One strong suit of melt processing is the capacity for the jet to solidify rapidly into a fiber, thus fixing a particular structure into position. The ability to direct‐write complex and multiscaled architectures and structures has greatly contributed to a large number of recent studies, explicitly, toward fiber–hydrogel composites and fugitive inks, and has expanded into several biomedical applications such as cartilage, skin, periosteum, and cardiovascular tissue engineering. Following the footsteps of a publication that summarized melt electrowriting literature up to 2015, the most recent literature from then until now is reviewed to provide a continuous and comprehensive timeline that demonstrates the latest advances as well as new perspectives for this emerging technology. Microscale chemistry ; Materials science ; Microfluidics ; Nanotechnology ; Electrospinning ; Jet (fluid) ; Soft robotics ; Electrohydrodynamics ; Mechanical engineering ; Microtechnology ; Microelectronics ; Computer science ; Composite material ; Engineering ; Artificial intelligence ; Polymer ; Robot ; Aerospace engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Electrohydrodynamics and Fluid Dynamics
Authors: Seyed Mohamad Moosavi | Arunraj Chidambaram | Leopold Talirz | Maciej Harańczyk | Kyriakos C. Stylianou | Berend Smit
Source: Nature Communications (article, 2019-02-01)
Field: Chemistry — Inorganic Chemistry

Capturing chemical intuition in synthesis of metal-organic frameworks We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials. Intuition ; Computer science ; Biochemical engineering ; Artificial intelligence ; Cognitive science ; Engineering ; Psychology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Qiuyi Huang | Wenlang Li | Zhu Mao | Lunjun Qu | Yang Li | Hao Zhang | Tao Yu | Zhiyong Yang | Juan Zhao | Yi Zhang | Matthew P. Aldred | Zhenguo Chi
Source: Nature Communications (article, 2019-07-12)
Field: Chemistry — Inorganic Chemistry

An exceptionally flexible hydrogen-bonded organic framework with large-scale void regulation and adaptive guest accommodation abilities Flexible hydrogen-bonded organic frameworks (FHOFs) are quite rare but promising for applications in separation, sensing and host-guest chemistry. They are difficult to stabilize, making their constructions a major challenge. Here, a flexible HOF (named 8PN) with permanent porosity has been successfully constructed. Nine single crystals of 8PN with different pore structures are obtained, achieving a large-scale void regulation from 4.4% to 33.2% of total cell volume. In response to external stimuli, multimode reversible structural transformations of 8PN accompanied by changes in luminescence properties have been realized. Furthermore, a series of high-quality co-crystals containing guests of varying shapes, sizes, aggregation states and even amounts are obtained, showing that 8PN can adapt to different guests by regulating the molecular conformations and assembling forms of its building blocks. The unexpected flexibility of 8PN makes it a promising material for enriching the applications of existing porous materials. Porosity ; Void (composites) ; Materials science ; Nanotechnology ; Flexibility (engineering) ; Metal-organic framework ; Luminescence ; Chemical physics ; Chemistry ; Composite material ; Optoelectronics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Luminescence and Fluorescent Materials
Authors: Paul C. Jennings | Steen Lysgaard | Jens S. Hummelshøj | Tejs Vegge | Thomas Bligaard
Source: npj Computational Materials (article, 2019-04-10)
Field: Materials Science — Materials Chemistry

Genetic algorithms for computational materials discovery accelerated by machine learning Abstract Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations. Computer science ; Machine learning ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shruti Venkatram | Chiho Kim | Anand Chandrasekaran | Rampi Ramprasad
Source: Journal of Chemical Information and Modeling (article, 2019-09-23)
Field: Chemistry — Spectroscopy

Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers Solubility parameter models are widely used to select suitable solvents/nonsolvents for polymers in a variety of processing and engineering applications. In this study, we focus on two well-established models, namely, the Hildebrand and Hansen solubility parameter models. Both models are built on the basis of the notion of "like dissolves like" and identify a liquid as a good solvent for a polymer if the solubility parameters of the liquid and the polymer are close to each other. Here we make a critical and quantitative assessment of the accuracy/utility of these two models by comparing their predictions against actual experimental data. Using a data set of 75 polymers, we find that the Hildebrand model displays a predictive accuracy of 60% for solvents and 76% for nonsolvents. The Hansen model leads to a similar performance; on the basis of a data set of 25 polymers for which Hansen parameters are available, we find that it has an accuracy of 67% for solvents and 76% for nonsolvents. The availability of the Hildebrand parameters for a large polymer data set makes it a widely applicable capability, as the Hildebrand parameter for a new polymer may be determined using this data set and machine learning methods as we have done before; the predicted Hildebrand parameter for a new polymer may then be used to determine suitable solvents and nonsolvents. Such predictions are difficult to make with the Hansen model, as the data set of Hansen parameters for polymers is rather small. Nevertheless, the Hildebrand approach must be used with caution. Our analysis shows that while the Hildebrand model has a predictive accuracy of 70-75% for nonpolar polymers, it performs rather poorly for polar polymers (with an accuracy of 57%). Going forward, determination of solvents and nonsolvents for polymers may benefit by developing classification models built directly on the basis of available experimental data sets rather than utilizing the solubility parameter approach, which is limited in versatility and accuracy. Hildebrand solubility parameter ; Solubility ; Polymer ; Flory–Huggins solution theory ; Solvent ; Set (abstract data type) ; Thermodynamics ; Chemistry ; Mathematics ; Computer science ; Organic chemistry ; Physics Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Surfactants and Colloidal Systems
Authors: Sherif Fakher | Mohamed Ahdaya | Mukhtar Elturki | Abdulmohsin Imqam
Source: Journal of Petroleum Exploration and Production Technology (article, 2019-12-05)
Field: Chemistry — Analytical Chemistry

Critical review of asphaltene properties and factors impacting its stability in crude oil Abstract Asphaltene is a component of crude oil that has been reported to cause severe problems during production and transportation of the oil from the reservoir. It is a solid component of the oil that has different structures and molecular makeup which makes it one of the most complex components of the oil. This research provides a detailed review of asphaltene properties, characteristics, and previous studies to construct a guideline to asphaltene and its impact on oil recovery. The research begins with an explanation of the main components of crude oil and their relation to asphaltene. The method by which asphaltene is quantified in the crude oil is then explained. Due to its different structures, asphaltene has been modeled using different models all of which are then discussed. All chemical analysis methods that have been used to characterize and study asphaltene are then mentioned and the most commonly used method is shown. Asphaltene will pass through several phases in the reservoir beginning from its stability phase up to its deposition in the pores, wellbore, and facilities. All these phases are explained, and the reason they may occur is mentioned. Following this, the methods by which asphaltene can damage oil recovery are presented. Asphaltene rheology and flow mechanism in the reservoir are then explained in detail including asphaltene onset pressure determination and significance and the use of micro- and nanofluidics to model asphaltene. Finally, the mathematical models, previous laboratory, and oilfield studies conducted to evaluate asphaltene are discussed. This research will help increase the understanding of asphaltene and provide a guideline to properly study and model asphaltene in future studies. Asphaltene ; Petroleum engineering ; Crude oil ; Rheology ; Petroleum ; Wellbore ; Offshore geotechnical engineering ; Materials science ; Chemistry ; Geology ; Organic chemistry ; Geotechnical engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Olga Kononova | Haoyan Huo | Tanjin He | Ziqin Rong | Tiago Botari | Wenhao Sun | Vahe Tshitoyan | Gerbrand Ceder
Source: Scientific Data (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

Text-mined dataset of inorganic materials synthesis recipes Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of "codified recipes" for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis. Bottleneck ; Computer science ; Computation ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jesús del Barrio | Carlos Sánchez‐Somolinos
Source: Advanced Optical Materials (article, 2019-06-06)
Field: Materials Science — Materials Chemistry

Light to Shape the Future: From Photolithography to 4D Printing Abstract Over the last few decades, the demand of polymeric structures with well‐defined features of different size, dimension, and functionality has increased from various application areas, including microelectronics, biotechnology, tissue engineering, and photonics, among others. The ability of light to control over space and time physicochemical processes is a unique tool for the structuring of polymeric materials, opening new avenues for technological progress in different fields of application. This article gives an overview of various photochemical reactions in polymers, photosensitive materials, and structuring techniques making use of light, and highlights most recent advances, emerging opportunities, and relevant applications. Structuring ; Microelectronics ; Nanotechnology ; Materials science ; Photolithography ; Photonics ; Dimension (graph theory) ; Biochemical engineering ; Computer science ; Engineering ; Optoelectronics Photochromic and Fluorescence Chemistry ; Nanofabrication and Lithography Techniques ; Advanced Polymer Synthesis and Characterization
Authors: Shoeb Anwar Ansari | Eleonora Ficiarà | Federico Alessandro Ruffinatti | Ilaria Stura | Monica Argenziano | Ornella Abollino | Roberta Cavalli | Caterina Guiot | Federico D’Agata
Source: Materials (review, 2019-02-02)
Field: Materials Science — Biomaterials

Magnetic Iron Oxide Nanoparticles: Synthesis, Characterization and Functionalization for Biomedical Applications in the Central Nervous System Magnetic Nanoparticles (MNPs) are of great interest in biomedicine, due to their wide range of applications. During recent years, one of the most challenging goals is the development of new strategies to finely tune the unique properties of MNPs, in order to improve their effectiveness in the biomedical field. This review provides an up-to-date overview of the methods of synthesis and functionalization of MNPs focusing on Iron Oxide Nanoparticles (IONPs). Firstly, synthesis strategies for fabricating IONPs of different composition, sizes, shapes, and structures are outlined. We describe the close link between physicochemical properties and magnetic characterization, essential to developing innovative and powerful magnetic-driven nanocarriers. In conclusion, we provide a complete background of IONPs functionalization, safety, and applications for the treatment of Central Nervous System disorders. Surface modification ; Nanotechnology ; Magnetic nanoparticles ; Nanocarriers ; Iron oxide nanoparticles ; Nanoparticle ; Characterization (materials science) ; Materials science ; Biomedicine ; Iron oxide ; Nanomedicine ; Engineering ; Chemical engineering ; Bioinformatics Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Graphene and Nanomaterials Applications
Authors: Péter Bélteky | Andrea Rónavári | Nóra Igaz | Bettina Szerencsés | Ildikó Y. Tóth | Ilona Pfeiffer | Mónika Kiricsi | Zoltán Kónya
Source: International Journal of Nanomedicine (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;Silver nanoparticles: aggregation behavior in biorelevant conditions and its impact on biological activity&lt;/p&gt; To our knowledge, this is the first systematic examination regarding AgNP aggregation behavior with simultaneous measurements of its effect on biological activity. We showed that nanoparticle behavior in complex systems can be estimated by simple compounds like sodium chloride and glutamine. Electrostatic stabilization might not be suitable for biomedical AgNP applications, while green synthesis approaches could offer new frontiers to preserve nanoparticle toxicity by enhancing colloidal stability. The importance of properly selected synthesis methods must be emphasized as they profoundly influence colloidal stability, and therefore biological activity. Silver nanoparticle ; Chemistry ; Nanoparticle ; Particle size ; Crystallinity ; Reducing agent ; Biomolecule ; Nanotechnology ; Materials science ; Biochemistry ; Organic chemistry Nanoparticles: synthesis and applications