jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Lederer | Waldemar Kaiser | Alessandro Mattoni | Alessio Gagliardi
Source: Advanced Theory and Simulations (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

Machine Learning–Based Charge Transport Computation for Pentacene Abstract Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as the intermolecular transfer integral, using quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)–based multiscale approach is presented that allows to simulate charge transport in organic semiconductors considering the static disorder within disordered crystals. By mapping fingerprints of dimers to their respective transfer integral, a kernel ridge regression ML algorithm for the prediction of charge transfer integrals is trained and evaluated. Since QC calculations of the electronic structure must be performed only once, the use of ML reduces the computation time radically, while maintaining the prediction error small. Transfer integrals predicted by ML are utilized for the computation of charge carrier mobilities using off‐lattice kinetic Monte Carlo (kMC) simulations. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations. The multiscale system is tested with the well‐known molecular semiconductor pentacene. The presented methodology allows reproducing the experimentally observed anisotropy of the mobility and enables a fast estimation of the impact of disorder. Pentacene ; Charge (physics) ; Computation ; Organic semiconductor ; Statistical physics ; Anisotropy ; Kernel (algebra) ; Kinetic Monte Carlo ; Intermolecular force ; Monte Carlo method ; Semiconductor ; Materials science ; Computer science ; Lattice (music) ; Chemical physics ; Algorithm ; Chemistry ; Physics ; Mathematics ; Transistor ; Quantum mechanics ; Molecule ; Optoelectronics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Molecular Junctions and Nanostructures
Authors: Mengshan Li | Huaijing Zhang | Bingsheng Chen | Wu Yan | Lixin Guan
Source: Scientific Reports (article, 2018-02-27)
Field: Chemistry — Organic Chemistry

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships. Particle swarm optimization ; Mean squared error ; Computer science ; Artificial neural network ; Population ; Entropy (arrow of time) ; Divergence (linguistics) ; Cross entropy ; Correlation coefficient ; Kullback–Leibler divergence ; Artificial intelligence ; Mathematics ; Principle of maximum entropy ; Algorithm ; Statistics ; Machine learning Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Peter Bjørn Jørgensen | Karsten W. Jacobsen | Mikkel N. Schmidt
Source: arXiv (Cornell University) (article, 2018-06-08)
Field: Materials Science — Materials Chemistry

Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we extend the neural message passing model with an edge update network which allows the information exchanged between atoms to depend on the hidden state of the receiving atom. We benchmark the proposed model on three publicly available datasets (QM9, The Materials Project and OQMD) and show that the proposed model yields superior prediction of formation energies and other properties on all three datasets in comparison with the best published results. Furthermore we investigate different methods for constructing the graph used to represent crystalline structures and we find that using a graph based on K-nearest neighbors achieves better prediction accuracy than using maximum distance cutoff or the Voronoi tessellation graph. Enhanced Data Rates for GSM Evolution ; Message passing ; Computer science ; Message broker ; Artificial neural network ; Artificial intelligence ; Computer network ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ajay Vikram Singh | Donato Gemmati | Anurag Kanase | Ishan Pandey | Vatsala Misra | Vimal Kishore | Timotheus Jahnke | Joachim Bill
Source: Veins and Lymphatics (review, 2018-02-20)
Field: Materials Science — Biomaterials

Nanobiomaterials for vascular biology and wound management: A review Nanobiomaterials application into tissue repair and ulcer management is experiencing its golden age due to spurring diversity of translational opportunity to clinics. Over the past years, research in clinical science has seen a dramatic increase in medicinal materials at nanoscale those significantly contributed to tissue repair. This chapter outlines the new biomaterials at nanoscale those contribute state of the art clinical practices in ulcer management and wound healing due to their superior properties over traditional dressing materials. Designing new recipes for nanobiomaterials for tissue engineering practices spanning from micro to nano-dimension provided an edge over traditional wound care materials those mimic tissue in vivo. Clinical science stepped into design of artificial skin and extracellular matrix components emulating the innate structures with higher degree of precision. Advances in materials sciences polymer chemistry have yielded an entire class of new nanobiomaterials ranging from dendrimer to novel electrospun polymer with biodegradable chemistries and controlled molecular compositions assisting wound healing adhesives, bandages and controlled of therapeutics in specialized wound care. Moreover, supportive regenerative medicine is transforming into rational, real and successful component of modern clinics providing viable cell therapy of tissue remodeling. Soft nanotechnology involving hydrogel scaffold revolutionized the wound management supplementing physicobiochemical and mechanical considerations of tissue regeneration. Moreover, this chapter also reviews the current challenges and opportunities in specialized nanobiomaterials formulations those are desirable for optimal localized wound care considering their in situ physiological microenvironment. Tissue engineering ; Wound healing ; Medicine ; Regenerative medicine ; Wound dressing ; Regeneration (biology) ; Scaffold ; Wound care ; Extracellular matrix ; Biomedical engineering ; Nanotechnology ; Surgery ; Materials science ; Stem cell ; Cell biology ; Biology Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; Wound Healing and Treatments
Authors: Sylvio Barbon | Ana Paula Ayub da Costa Barbon | Rafael Gomes Mantovani | Douglas Fernandes Barbin
Source: Journal of Spectroscopy (article, 2018-08-07)
Field: Chemistry — Analytical Chemistry

Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIE<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math>, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mn>77.2</mml:mn><mml:mtext>%</mml:mtext></mml:mrow></mml:math> precision. The selected wavelengths could lead to potential simple multispectral acquisition devices. Support vector machine ; Computer science ; Artificial intelligence ; Algorithm ; Multispectral image ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Filipe A. Snel | Jez Willian Batista Braga | Diego da Silva | Alex C. Wiedenhoeft | Adriana Costa | Richard Soares | Vera T.R. Coradin | Tereza Cristina Monteiro Pastore
Source: Wood Science and Technology (article, 2018-06-22)
Field: Chemistry — Organic Chemistry

Potential field-deployable NIRS identification of seven Dalbergia species listed by CITES Dalbergia sissoo ; Dalbergia ; Identification (biology) ; Hardwood ; Mathematics ; Linear discriminant analysis ; Class (philosophy) ; Discriminant ; Botany ; Artificial intelligence ; Computer science ; Statistics ; Biology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Spectroscopy and Chemometric Analyses
Authors: Jhonatan R. Guarín | Juan Carlos Moreno‐Piraján | Liliana Giraldo Gutiérrez
Source: C – Journal of Carbon Research (article, 2018-09-20)
Field: Chemistry — Spectroscopy

Kinetic and Equilibrium Study of the Adsorption of CO2 in Ultramicropores of Resorcinol-Formaldehyde Aerogels Obtained in Acidic and Basic Medium In this work, aerogels were prepared using resorcinol-formaldehyde as a precursor in two synthetic routes, one basic and one acidic, to perform the adsorption of CO2 at 0 °C and atmospheric pressure. Aerogels were Characterization by N2 and CO2 Physisorption, Raman Spectroscopy, Scanning Electron Microscopy, and Infrared Spectroscopy. In general, was found that aerogels have a polymeric, disordered, three-dimensional structure and have a microporous surface. Langmuir, Freundlich, Sips and Toth equilibrium models present a good data fit of CO2 adsorption at relative pressure ranging between 1 × 10−4 and 3 × 10−2. The diffusion intra-particle kinetic model explains the setps of this process; the Elovich model also showed a good fit, therefore, there are an energetic heterogeneity of the CO2 superficial adsorption sites. The aerogel carbonized in basic medium at 1050 °C (ACB 1050) material was the best adsorbent of this pollutant, reaching an adsorption capacity of 6.43 mmol g−1. Adsorption ; Resorcinol ; Langmuir ; Microporous material ; Chemical engineering ; Physisorption ; Chemistry ; Aerogel ; Carbonization ; Freundlich equation ; Formaldehyde ; Diffusion ; Materials science ; Organic chemistry ; Thermodynamics Aerogels and thermal insulation ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics
Authors: Ting Xu | Yan He | Ying Qin | Chengxi Zhao | Changjun Peng | Jun Hu | Honglai Liu
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Inorganic Chemistry

Facile preparation of porous organic copolymer based on triptycene and crown ether for efficient organic dye adsorption There has been great interest in the use of porous polymers to remove organic dyes because of their adjustable surface area and task-specific functionality. We chose a triptycene-based porous polymer to ensure high porosity, and introduced crown ether into the sketch of the copolymer to significantly enhance the affinity for the organic dye molecules. Novel porous organic copolymers of triptycene and crown-ether-15 (POP-TCE-15) were obtained by a simple Friedel-Crafts reaction, and were highly effective in removing organic dyes from aqueous solution. POP-TCE-15 exhibited the best performance, with a maximum adsorption capacity of methylene blue, rhodamine B, and methyl orange of 787.4 mg g<sup>-1</sup>, 421.9 mg g<sup>-1</sup>, and 64.8 mg g<sup>-1</sup>, respectively, which is better than most reported adsorbents. Their adsorption rates and adsorption isotherms were well fitted with pseudo-second-order kinetic models and the Langmuir model. More importantly, POP-TCE-15 can be effectively regenerated and recycled at least 5 times without any loss of adsorption capacity. With a hierarchical porous structure, high surface area, high hydrophobicity, and excellent adsorption capacity for dyes, the POP-TCE polymers could be ideal adsorbents for water purification and treatment. Adsorption ; Rhodamine B ; Crown ether ; Copolymer ; Triptycene ; Chemical engineering ; Aqueous solution ; Acid dye ; Ether ; Polymer ; Polymer chemistry ; Langmuir adsorption model ; Methyl orange ; Resorcinol ; Porosity ; Materials science ; Chemistry ; Organic chemistry ; Dyeing ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Yixiu Wang | Raquel Ferreira | Ruoxing Wang | Gang Qiu | Gaoda Li | Yong Qin | Peide D. Ye | Arman Sabbaghi | Wenzhuo Wu
Source: Nano Energy (article, 2018-12-24)
Field: Materials Science — Materials Chemistry

Data-driven and probabilistic learning of the process-structure-property relationship in solution-grown tellurene for optimized nanomanufacturing of high-performance nanoelectronics Nanomanufacturing ; Nanoelectronics ; Materials science ; Property (philosophy) ; Process (computing) ; Probabilistic logic ; Nanotechnology ; Artificial intelligence ; Computer science 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Nitin K. Hansoge | Tianyu Huang | Robert Sinko | Wenjie Xia | Wei Chen | Sinan Keten
Source: ACS Nano (article, 2018-07-05)
Field: Materials Science — Biomaterials

Materials by Design for Stiff and Tough Hairy Nanoparticle Assemblies Matrix-free polymer-grafted nanocrystals, called assembled hairy nanoparticles (aHNPs), can significantly enhance the thermomechanical performance of nanocomposites by overcoming nanoparticle dispersion challenges and achieving stronger interfacial interactions through grafted polymer chains. However, effective strategies to improve both the mechanical stiffness and toughness of aHNPs are lacking given the general conflicting nature of these two properties and the large number of molecular parameters involved in the design of aHNPs. Here, we propose a computational framework that combines multiresponse Gaussian process metamodeling and coarse-grained molecular dynamics simulations to establish design strategies for achieving optimal mechanical properties of aHNPs within a parametric space. Taking poly(methyl methacrylate) grafted to high-aspect-ratio cellulose nanocrystals as a model nanocomposite, our multiobjective design optimization framework reveals that the polymer chain length and grafting density are the main influencing factors governing the mechanical properties of aHNPs, in comparison to the nanoparticle size and the polymer-nanoparticle interfacial interactions. In particular, the Pareto frontier, that marks the upper bound of mechanical properties within the design parameter space, can be achieved when the weight percentage of nanoparticles is above around 60% and the grafted chains exceed the critical length scale governing transition into the semidilute brush regime. We show that theoretical scaling relationships derived from the Daoud-Cotton model capture the dependence of the critical length scale on graft density and nanoparticle size. Our established modeling framework provides valuable insights into the mechanical behavior of these hairy nanoparticle assemblies at the molecular level and allows us to establish guidelines for nanocomposite design. Materials science ; Nanoparticle ; Nanocomposite ; Toughness ; Polymer nanocomposite ; Polymer ; Composite material ; Nanotechnology Advanced Cellulose Research Studies ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization
Authors: Camilla María Cova | Alessio Zuliani | Alain R. Puente Santiago | Álvaro Caballero | Mario J. Muñoz‐Batista | Rafael Luque
Source: Journal of Materials Chemistry A (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Microwave-assisted preparation of Ag/Ag<sub>2</sub>S carbon hybrid structures from pig bristles as efficient HER catalysts A microwave-assisted method for the production of HER catalyst Ag/Ag<sub>2</sub>S carbon hybrids through the valorisation of pig bristles is described. Catalysis ; Bristle ; Carbon fibers ; Microwave ; Materials science ; Valorisation ; Nanotechnology ; Chemistry ; Waste management ; Organic chemistry ; Composite material ; Engineering ; Telecommunications ; Composite number Nanomaterials for catalytic reactions ; Catalysis for Biomass Conversion ; Catalytic Processes in Materials Science
Authors: Tomoki Nishimura | Kazunari Akiyoshi
Source: Advanced Science (article, 2018-09-19)
Field: Materials Science — Biomaterials

Biotransporting Biocatalytic Reactors toward Therapeutic Nanofactories Drug-delivery systems (DDSs), in which drug encapsulation in nanoparticles enables targeted delivery of therapeutic agents and their release at specific disease sites, are important because they improve drug efficacy and help to decrease side effects. Although significant progress has been made in the development of DDSs for the treatment of a wide range of diseases, new approaches that increase the scope and effectiveness of such systems are still needed. Concepts such as nanoreactors and nanofactories are therefore attracting much attention. Nanoreactors, which basically consist of vesicle-encapsulated enzymes, provide prodrug conversion to therapeutic agents rather than simple drug delivery. Nanofactories are an extension of this concept and combine the features of nanoreactors and delivery carriers. Here, the required features of nanofactories are discussed and an overview of current strategies for the design and fabrication of different types of nanoreactors, i.e., systems based on lipid or polymer vesicles, capsules, mesoporous silica, viral capsids, and hydrogels, and their respective advantages and shortcomings, is provided. In vivo applications of biocatalytic reactors in the treatment of cancer, glaucoma, neuropathic pain, and alcohol intoxication are also discussed. Finally, the prospects for further progress in this important and promising field are outlined. Nanoreactor ; Nanotechnology ; Drug delivery ; Polymersome ; Drug ; Materials science ; Medicine ; Pharmacology ; Nanoparticle ; Polymer Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics
Authors: Aleksandar Bijelic | Annette Rompel
Source: ChemTexts (article, 2018-08-28)
Field: Materials Science — Materials Chemistry

Polyoxometalates: more than a phasing tool in protein crystallography Protein crystallography is the most widely used method for determining the molecular structure of proteins and obtaining structural information on protein-ligand complexes at the atomic level. As the structure determines the functions and properties of a protein, crystallography is of immense importance for nearly all research fields related to biochemistry. However, protein crystallography suffers from some major drawbacks, whereby the unpredictability of the crystallization process represents the main bottleneck. Crystallization is still more or less a 'trial and error' based procedure, and therefore, very time and resource consuming. Many strategies have been developed in the past decades to improve or enable the crystallization of proteins, whereby the use of so-called additives, which are mostly small molecules that make proteins more amenable to crystallization, is one of the most convenient and successful methods. Most of the commonly used additives are, however, restricted to particular crystallization conditions or groups of proteins. Therefore, a more universal additive addressing a wider range of proteins and being applicable to a broad spectrum of crystallization conditions would represent a significant advance in the field of protein crystallography. In recent years, polyoxometalates (POMs) emerged as a promising group of crystallization additives due to their unique structures and properties. In this regard, the tellurium-centered Anderson-Evans polyoxotungstate [TeW<sub>6</sub>O<sub>24</sub>]<sup>6-</sup> (TEW) showed its high potential as crystallization additive. In this lecture text, the development of POMs as tools in protein crystallography are discussed with a special focus on the so far most successful cluster TEW. Crystallization ; Protein crystallization ; Crystallography ; Chemistry ; Computer science ; Materials science ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Vanadium and Halogenation Chemistry ; Metal-Organic Frameworks: Synthesis and Applications