jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aowabin Rahman | Prathamesh Deshpande | Matthew S. Radue | Gregory M. Odegard | S. Gowtham | Susanta Ghosh | Ashley D. Spear
Source: Composites Science and Technology (article, 2020-12-22)
Field: Materials Science — Materials Chemistry

A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data Materials science ; Carbon nanotube ; Composite material ; Epoxy ; Molecular dynamics ; Polymer ; Surrogate model ; Stiffness ; Shear strength (soil) ; Aerospace ; Computer science ; Aerospace engineering ; Machine learning Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites
Authors: Kamal Choudhary | Kevin F. Garrity | Vinit Sharma | Adam J. Biacchi | Angela R. Hight Walker | Francesca Tavazza
Source: npj Computational Materials (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively. We identify 577 high-piezoelectric materials, using a threshold of 0.5 C/m<sup>2</sup>. Using a threshold of 20, we find 593 potential high-dielectric materials. Importantly, we analyze the chemistry, symmetry, dimensionality, and geometry of the materials to find features that help explain variations in our datasets. Finally, we develop high-accuracy regression models for the highest infrared frequency and maximum Born-effective charges, and classification models for maximum piezoelectric and average dielectric tensors to accelerate discovery. Dielectric ; Piezoelectricity ; Perturbation theory (quantum mechanics) ; Infrared ; Density functional theory ; Phonon ; Curse of dimensionality ; Materials science ; Computer science ; Condensed matter physics ; Computational physics ; Statistical physics ; Machine learning ; Physics ; Optoelectronics ; Optics ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Thermoelectric Materials and Devices
Authors: Pavlo O. Dral | A. Owens | Alexey Dral | Gábor Cśanyi
Source: The Journal of Chemical Physics (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Hierarchical machine learning of potential energy surfaces We present hierarchical machine learning (hML) of highly accurate potential energy surfaces (PESs). Our scheme is based on adding predictions of multiple Δ-machine learning models trained on energies and energy corrections calculated with a hierarchy of quantum chemical methods. Our (semi-)automatic procedure determines the optimal training set size and composition of each constituent machine learning model, simultaneously minimizing the computational effort necessary to achieve the required accuracy of the hML PES. Machine learning models are built using kernel ridge regression, and training points are selected with structure-based sampling. As an illustrative example, hML is applied to a high-level ab initio CH<sub>3</sub>Cl PES and is shown to significantly reduce the computational cost of generating the PES by a factor of 100 while retaining similar levels of accuracy (errors of ∼1 cm<sup>-1</sup>). Computer science ; Machine learning ; Artificial intelligence ; Hierarchy ; Kernel (algebra) ; Energy (signal processing) ; Set (abstract data type) ; Regression ; Ridge ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yun Zhang | Xiaojie Xu
Source: Applied Physics A (article, 2020-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning the magnetocaloric effect in manganites from lattice parameters Magnetic refrigeration ; Orthorhombic crystal system ; Condensed matter physics ; Materials science ; Lattice (music) ; Refrigeration ; Thermodynamics ; Machine learning ; Magnetization ; Crystal structure ; Magnetic field ; Computer science ; Physics ; Chemistry ; Crystallography Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Advanced Thermoelectric Materials and Devices
Authors: Yang Yu | Ting‐Hui Xiao | Yunzhao Wu | Wanjun Li | Qing-Guang Zeng | Long Li | Zhi‐Yuan Li
Source: Advanced Photonics (article, 2020-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Roadmap for single-molecule surface-enhanced Raman spectroscopy In the near future, single-molecule surface-enhanced Raman spectroscopy (SERS) is expected to expand the family of popular analytical tools for single-molecule characterization. We provide a roadmap for achieving single molecule SERS through different enhancement strategies for diverse applications. We introduce some characteristic features related to single-molecule SERS, such as Raman enhancement factor, intensity fluctuation, and data analysis. We then review recent strategies for enhancing the Raman signal intensities of single molecules, including electromagnetic enhancement, chemical enhancement, and resonance enhancement strategies. To demonstrate the utility of single-molecule SERS in practical applications, we present several examples of its use in various fields, including catalysis, imaging, and nanoelectronics. Finally, we specify current challenges in the development of single-molecule SERS and propose corresponding solutions. Raman spectroscopy ; Molecule ; Nanoelectronics ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Materials science ; SIGNAL (programming language) ; Raman scattering ; Computer science ; Chemistry ; Physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Linda J. Johnston | Norma González‐Rojano | Kevin J. Wilkinson | Baoshan Xing
Source: NanoImpact (article, 2020-02-29)
Field: Materials Science — Materials Chemistry

Key challenges for evaluation of the safety of engineered nanomaterials The development of reliable and practical tools to ensure the safe and sustainable use of nanomaterials has not kept pace with the rapid commercialization of nanotechnology-enabled products. Here we present an overview of some of the current challenges and opportunities for the evaluation of the safety of engineered nanomaterials (ENM), based on input from an IUPAC (International Union of Pure and Applied Chemistry) sponsored workshop in Queretaro, Mexico. The workshop covered detection and characterization of ENM, their transformations in consumer products and the environment, current gaps in nanotoxicology methods and challenges for standardization and risk assessment. Key future research priorities include developing methods to detect and characterize ENM in complex matrices and determinations of their transformations in such environments. The extension of methods to more challenging materials such as complex or advanced nanomaterials and carbon-based materials is also needed. Furthermore, an increased availability of validated protocols and reference materials will be necessary to advance both nanosafety studies and material characterization. A focus on accurate dosimetry, appropriate control experiments and more realistic in vitro models will help to address current gaps in nanotoxicology. Finally, there is an urgent need for reliable data and improved data reporting guidelines, supported by computational modeling, in order to allow the development of grouping and read across methods. Commercialization ; Standardization ; Risk analysis (engineering) ; Computer science ; Nanotechnology ; Biochemical engineering ; Pace ; Systems engineering ; Data science ; Engineering ; Business ; Materials science Nanoparticles: synthesis and applications
Authors: Mikihiro Hayashi
Source: Polymers (review, 2020-06-10)
Field: Materials Science — Polymers and Plastics

Implantation of Recyclability and Healability into Cross-Linked Commercial Polymers by Applying the Vitrimer Concept Vitrimers are a new class of cross-linked materials that are capable of network topology alternation through the associative dynamic bond-exchange mechanism, which has recently been invented to solve the problem of conventional cross-linked materials, such as poor recyclability and healability. Thus far, the concept of vitrimers has been applied to various commercial polymers, e.g., polyesters, polylactides, polycarbonates, polydimethylsiloxanes, polydienes, polyurethanes, polyolefins, poly(meth)acrylates, and polystyrenes, by utilizing different compatible bond-exchange reactions. In this review article, the concept of vitrimers is described by clarifying the difference from thermoplastics and supramolecular systems; in addition, the term "associative bond-exchange" in vitrimers is explained by comparison with the "dissociative" term. Several useful functions attained by the vitrimer concept (including recyclability and healability) are demonstrated, and recent molecular designs of vitrimers are classified into groups depending on the types of molecular frameworks. This review specifically focuses on the vitrimer molecular designs with commercial polymer-based frameworks, which provide useful hints for the practical application of the vitrimer concept. Polymer science ; Materials science ; Polymer ; Forensic engineering ; Engineering ; Composite material Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties
Authors: Xijun Wang | Sheng Ye | Wei Hu | Edward Sharman | Ran Liu | Yan Liu | Yi Luo | Jun Jiang
Source: Journal of the American Chemical Society (article, 2020-04-16)
Field: Materials Science — Materials Chemistry

Electric Dipole Descriptor for Machine Learning Prediction of Catalyst Surface–Molecular Adsorbate Interactions The challenge of evaluating catalyst surface-molecular adsorbate interactions holds the key for rational design of catalysts. Finding an experimentally measurable and theoretically computable descriptor for evaluating surface-adsorbate interactions is a significant step toward achieving this goal. Here we show that the electric dipole moment can serve as a convenient yet accurate descriptor for establishing structure-property relationships for molecular adsorbates on metal catalyst surfaces. By training a machine learning neural network with a large data set of first-principles calculations, we achieve quick and accurate predictions of molecular adsorption energy and transferred charge. The training model using NO/CO@Au(111) can be extended to study additional substrates such as Au(001) or Ag(111), thus exhibiting extraordinary transferability. These findings validate the effectiveness of the electric dipole descriptor, providing an efficient modality for future catalyst design. Chemistry ; Dipole ; Molecular descriptor ; Catalysis ; Transferability ; Surface (topology) ; Adsorption ; Artificial neural network ; Moment (physics) ; Chemical physics ; Electric dipole moment ; Computational chemistry ; Artificial intelligence ; Machine learning ; Quantitative structure–activity relationship ; Physical chemistry ; Computer science ; Quantum mechanics ; Organic chemistry ; Physics ; Stereochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Daniel Ziental | Beata Czarczyńska-Goślińska | Dariusz T. Młynarczyk | Arleta Glowacka-Sobotta | Beata Stanisz | Tomasz Gośliński | Łukasz Sobotta
Source: Preprints.org (preprint, 2020-02-19)
Field: Materials Science — Materials Chemistry

Titanium Dioxide Nanoparticles &amp;ndash; Prospects and Applications in Medicine Metallic nanoparticles (NPs), among polymeric NPs, liposomes, micelles, quantum dots, dendrimers, or fullerenes, are becoming more and more important due to their potential use in the novel medical therapies. Titanium dioxide (titanium(IV) oxide, titania, TiO2) is an inorganic compound that owes its recent rise in scientific interest to photoactivity. After the illumination in aqueous media with UV light, TiO2 produces an array of reactive oxygen species (ROS). The capability to produce ROS and thus induce cell death has found application in the photodynamic therapy (PDT) for the treatment of a wide range of maladies, from psoriasis to cancer. Titanium dioxide NPs were studied as photosensitizing agents in the treatment of malignant tumors as well as in photodynamic inactivation of antibiotic-resistant bacteria. Both TiO2 NPs themselves, as well as their composites with other molecules, can be successfully used as photosensitizers in PDT. Moreover, various organic compounds can be grafted on TiO2 NPs, leading to hybrid materials. These nanostructures can reveal increased light absorption allowing their further use in targeted therapy in medicine. In order to improve efficient anticancer therapy, many approaches utilizing titanium dioxide were tested. The most significant studies are discussed in this review. Titanium dioxide ; Photodynamic therapy ; Nanoparticle ; Nanotechnology ; Materials science ; Nanomedicine ; Titanium oxide ; Titanium ; Photocatalysis ; Reactive oxygen species ; Chemistry ; Organic chemistry ; Catalysis Advanced Nanomaterials in Catalysis
Authors: Alessandra Biancolillo | Federico Marini | Cyril Ruckebusch | Raffaele Vitale
Source: Applied Sciences (article, 2020-09-18)
Field: Chemistry — Analytical Chemistry

Chemometric Strategies for Spectroscopy-Based Food Authentication In the last decades, spectroscopic techniques have played an increasingly crucial role in analytical chemistry, due to the numerous advantages they offer. Several of these techniques (e.g., Near-InfraRed—NIR—or Fourier Transform InfraRed—FT-IR—spectroscopy) are considered particularly valuable because, by means of suitable equipment, they enable a fast and non-destructive sample characterization. This aspect, together with the possibility of easily developing devices for on- and in-line applications, has recently favored the diffusion of such approaches especially in the context of foodstuff quality control. Nevertheless, the complex nature of the signal yielded by spectroscopy instrumentation (regardless of the spectral range investigated) inevitably calls for the use of multivariate chemometric strategies for its accurate assessment and interpretation. This review aims at providing a comprehensive overview of some of the chemometric tools most commonly exploited for spectroscopy-based foodstuff analysis and authentication. More in detail, three different scenarios will be surveyed here: data exploration, calibration and classification. The main methodologies suited to addressing each one of these different tasks will be outlined and examples illustrating their use will be provided alongside their description. Context (archaeology) ; Computer science ; Chemometrics ; Biochemical engineering ; Engineering ; Machine learning ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mingkun Gao | Yuhang Gao | Ge Chen | Xiaodong Huang | Xiaomin Xu | Jun Lv | Jing Wang | Donghui Xu | Guangyang Liu
Source: Frontiers in Chemistry (review, 2020-12-01)
Field: Chemistry — Analytical Chemistry

Recent Advances and Future Trends in the Detection of Contaminants by Molecularly Imprinted Polymers in Food Samples Drug residues, organic dyes, heavy metals, and other chemical pollutants not only cause environmental pollution, but also have a serious impact on food safety. Timely and systematic summary of the latest scientific advances is of great importance for the development of new detection technologies. In particular, molecularly imprinted polymers (MIPs) can mimic antibodies, enzymes and other biological molecules to recognize, enrich, and separate contaminants, with specific recognition, selective adsorption, high affinity, and strong resistance characteristics. Therefore, MIPs have been widely used in chemical analysis, sensing, and material adsorption. In this review, we first describe the basic principles and production processes of molecularly imprinted polymers. Secondly, an overview of recent applications of molecularly imprinted polymers in sample pre-treatment, sensors, chromatographic separation, and mimetic enzymes is highlighted. Finally, a brief assessment of current technical issues and future trends in molecularly imprinted polymers is also presented. Molecularly imprinted polymer ; Molecular imprinting ; Nanotechnology ; Adsorption ; Polymer ; Biochemical engineering ; Environmental pollution ; Heavy metals ; Environmental chemistry ; Environmental science ; Chemistry ; Materials science ; Organic chemistry ; Selectivity ; Engineering ; Environmental protection Analytical chemistry methods development ; Biosensors and Analytical Detection ; Molecular Sensors and Ion Detection
Authors: Xiaofei Zhou | Hainan Sun | Xue Bai
Source: Frontiers in Bioengineering and Biotechnology (review, 2020-04-07)
Field: Materials Science — Materials Chemistry

Two-Dimensional Transition Metal Dichalcogenides: Synthesis, Biomedical Applications and Biosafety Evaluation Recently, two-dimensional transition metal dichalcogenides (2D TMDCs) have drawn certain attentions in many fields. The unique and diversified electronic structure and ultrathin sheet structure of 2D TMDCs offer opportunities for moving ahead of other 2D nanomaterials such as graphene and expanding the wide application of inorganic 2D nanomaterials in many fields. For a better understanding of 2D TMDCs, one needs to know methods for their synthesis and modification, as well as their potential applications and possible biological toxicity. Herein, we summarized the recent research progress of 2D TMDCs with particular focus on their biomedical applications and potential health risks. Firstly, two kinds of synthesis methods of 2D TMDCs, top-down and bottom-up, and methods for their surface functionalization are reviewed. Secondly, the applications of 2D TMDCs in the field of biomedicine, including drug loading, photothermal therapy, biological imaging and biosensor were summarized. After that, we presented the existing researches on biosafety evaluation of 2D TMDCs. At last, we discussed major research gap in current researches and challenges and coping strategies in future studies. Biosafety ; Nanotechnology ; Photothermal therapy ; Nanomaterials ; Biomedicine ; Materials science ; Computer science ; Medicine ; Bioinformatics 2D Materials and Applications ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: You Li | Yutao Zhao | Ting Zhou | Mengqing Chen | Yi‐Pan Li | Ming‐Yao Huang | Zhenchuang Xu | Shou‐Fei Zhu | Qi‐Lin Zhou
Source: Journal of the American Chemical Society (article, 2020-05-14)
Field: Chemistry — Organic Chemistry

Highly Enantioselective O–H Bond Insertion Reaction of α-Alkyl- and α-Alkenyl-α-diazoacetates with Water Catalytic asymmetric reactions in which water is a substrate are rare. Enantioselective transition-metal-catalyzed insertion of carbenes into the O-H bond of water can be used to incorporate water into the stereogenic center, but the reported chiral catalysts give good results only when α-aryl-α-diazoesters are used as the carbene precursors. Herein we report the first highly enantioselective O-H bond insertion reactions between water and α-alkyl- and α-alkenyl-α-diazoesters as carbene precursors, with catalysis by a combination of achiral dirhodium complexes and chiral phosphoric acids or chiral phosphoramides. Participation of the phosphoric acids or phosphoramides in the carbene transfer reaction markedly suppressed competing side reactions, such as β-H migration, carbene dimerization, and olefin isomerization, and thus ensured good yields of the desired products. Fine-tuning of the ester moiety facilitated enantiocontrol of the proton transfer reactions of the enol intermediates and resulted in excellent enantioselectivity. This protocol represents an efficient new method for preparation of multifunctionalized chiral α-alkyl and α-alkenyl hydroxyl esters, which readily undergo various transformations and can thus be used for the synthesis of bioactive compounds. Mechanistic studies revealed that the phosphoric acids and phosphoramides promoted highly enantioselective [1,2]- and [1,3]-proton transfer reactions of the enol intermediates. Maximization of molecular orbital overlap in the transition states of the proton transfer reactions was the original driving force to involve the proton shuttle catalysts in this process. Chemistry ; Enantioselective synthesis ; Alkyl ; Insertion reaction ; Medicinal chemistry ; Organic chemistry ; Stereochemistry ; Catalysis Cyclopropane Reaction Mechanisms ; Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods
Authors: Teboho Clement Mokhena | Mokgaotsa Jonas Mochane | Asanda Mtibe | Maya Jacob John | Emmanuel Rotimi Sadiku | Jeremia Shale Sefadi
Source: Materials (review, 2020-02-20)
Field: Materials Science — Biomaterials

Electrospun Alginate Nanofibers Toward Various Applications: A Review Alginate has been a material of choice for a spectrum of applications, ranging from metal adsorption to wound dressing. Electrospinning has added a new dimension to polymeric materials, including alginate, which can be processed to their nanosize levels in order to afford unique nanostructured materials with fascinating properties. The resulting nanostructured materials often feature high porosity, stability, permeability, and a large surface-to-volume ratio. In the present review, recent trends on electrospun alginate nanofibers from over the past 10 years toward advanced applications are discussed. The application of electrospun alginate nanofibers in various fields such as bioremediation, scaffolds for skin tissue engineering, drug delivery, and sensors are also elucidated. Electrospinning ; Nanofiber ; Materials science ; Nanotechnology ; Drug delivery ; Porosity ; Tissue engineering ; Adsorption ; Polymer ; Composite material ; Biomedical engineering ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications