jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ellen T. Swann | Baichuan Sun | Deidre Cleland | Amanda S. Barnard
Source: Molecular Simulation (article, 2018-04-02)
Field: Materials Science — Materials Chemistry

Representing molecular and materials data for unsupervised machine learning Statistical analysis and machine learning can help us understand and predict the collective properties and performance of ensembles of molecules and nanostructures, while accounting for all the complexity and diversity of real world specimens. Combining data-driven techniques with robust and reliable simulation methods can provide insights that cannot be made any other way. However, not all statistical and machine learning methods are right for all occasions; testing, validation and perhaps some trial and error are needed. Domain knowledge alone is not sufficient to choose the right algorithms. Data representation methods that are best suited to machine learning are not necessarily scientifically intuitive. The best descriptors are not always the structural features or physiochemical properties that we are aiming to control, and the way our data is distributed can be as important as what it contains. In this review, we discuss the differences, advantages and disadvantages of some of the common data representation, reduction and classification methods applicable to molecular and materials modelling. Focussing on unsupervised methods, we highlight features of these algorithms that determine their suitability and can inform choices of which learning method to use and how to effectively prepare data. A case study is also provided to demonstrate how testing can be undertaken, and how methods can be combined. Computer science ; Machine learning ; Artificial intelligence ; Representation (politics) ; Domain (mathematical analysis) ; Unsupervised learning ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Janis Timoshenko | Stefanie Roese | H. Hövel | Anatoly I. Frenkel
Source: Radiation Physics and Chemistry (article, 2018-11-05)
Field: Materials Science — Materials Chemistry

Silver clusters shape determination from in-situ XANES data XANES ; In situ ; Nanoparticle ; Absorption (acoustics) ; Materials science ; Ab initio ; Artificial neural network ; Particle (ecology) ; Chemical physics ; Nanotechnology ; Computer science ; Chemistry ; Spectroscopy ; Physics ; Artificial intelligence ; Geology ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Caroline Desgranges | Jérôme Delhommelle
Source: The Journal of Chemical Physics (article, 2018-07-27)
Field: Materials Science — Materials Chemistry

A new approach for the prediction of partition functions using machine learning techniques Using machine learning (ML), we predict the partition functions and, thus, all thermodynamic properties of atomic and molecular fluids over a wide range of temperatures and pressures. Our approach is based on training neural networks using, as a reference, the results of a few flat-histogram simulations. The neural network weights so obtained are then used to predict fluid properties that are shown to be in excellent agreement with the experiment and with simulation results previously obtained on argon, carbon dioxide, and water. In particular, the ML predictions for the Gibbs free energy, Helmholtz free energy, and entropy are shown to be highly accurate over a wide range of conditions and states for bulk phases as well as for the conditions of phase coexistence. Our ML approach thus provides access instantly to G, A, and S, thereby eliminating the need to carry out any additional simulations to explore the dependence of the fluid properties on the conditions of temperature and pressure. This is of particular interest, for e.g., the screening of new materials, as well as in the parameterization of force fields, for which this ML approach provides a rapid way to assess the impact of new sets of parameters on the system properties. Helmholtz free energy ; Artificial neural network ; Gibbs free energy ; Computer science ; Range (aeronautics) ; Statistical physics ; Histogram ; Partition (number theory) ; Phase transition ; Thermodynamics ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: C. Velmurugan | V. Senthilkumar
Source: Materials and Manufacturing Processes (article, 2018-09-05)
Field: Materials Science — Materials Chemistry

Optimization of spark plasma sintering parameters for NiTiCu shape memory alloys Nanostructured nickel titanium copper-shape memory alloys (NiTiCu-SMAs) were fabricated using spark plasma sintering (SPS) by varying the significant process parameters. The NiTiCu elements with different particle size were consolidated in a temperature range of 700–900°C and pressure from 20 to 40 MPa with 5 min of soaking time. The sintered products were subjected to mechanical analysis such as density and microhardness. Genetic algorithm (GA) and particle swarm optimization (PSO) techniques were used with integrated artificial neural network (ANN) to optimize the SPS process parameters to obtain better mechanical characteristics. The results indicate that the density and microhardness can be enhanced by the reduction of particle size and increase in pressure and temperature. A maximum density of 6.21 g/cc and Vickers hardness of 766 Hv were obtained the optimal for process parameters of temperature, pressure, and particle size of ~ 800°C, ~ 26 MPa and ~ 6 µm, respectively, in case of NiTiCu nanostructured SMAs. Materials science ; Spark plasma sintering ; Indentation hardness ; Particle swarm optimization ; Particle (ecology) ; Metallurgy ; Particle size ; Vickers hardness test ; Sintering ; Plasma ; Composite material ; Microstructure ; Algorithm ; Chemical engineering Shape Memory Alloy Transformations
Authors: Xin Tang | Yong Haur Tay | Nordahlia Abdullah Siam | Seng Choon Lim
Source:  (article, 2018-11-17)
Field: Chemistry — Organic Chemistry

MyWood-ID Wood serves as raw material for countless industries due to its unique material characteristics. As such, different types of wood are traded and valued accordingly based on their supply and demand. Hence, the correct identification of wood based on trading name is important in commercial trading. Macroscopic level wood identification that has been practiced by wood anatomists for decades can identify wood up to family level in some cases but mostly up to the genera level. However, the skill on wood identification is hardly transferable due to the complexity and variation of wood structures. In this paper, we proposed a rapid and robust macroscopic wood identification system using machine vision with deep learning method using smartphone and retrofitted macro-lens as effective image acquisition device. With cloud hosting, our system is cost effective, easily accessible, fast and scalable at the same time provides great accuracy on identification. This system is trained to identify 100 commonly trading wood types found in Malaysia with Top-1 accuracy of 77.52% and Top-2 accuracy of 87.29%. A beta version of the application can be downloaded from Apple Appstore. Identification (biology) ; Computer science ; Macro ; Cloud computing ; Scalability ; Artificial intelligence ; Wood industry ; Database ; Operating system Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Advanced Image and Video Retrieval Techniques
Authors: Matthew Shardlow | Meizhi Ju | Maolin Li | Christian O’Reilly | Elisabetta Iavarone | John McNaught | Sophia Ananiadou
Source: Neuroinformatics (article, 2018-11-15)
Field: Materials Science — Materials Chemistry

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments. Neuroinformatics ; Computer science ; Pipeline (software) ; Conditional random field ; Context (archaeology) ; Computational neuroscience ; Artificial intelligence ; Set (abstract data type) ; Deep learning ; Field (mathematics) ; Machine learning ; Data science Machine Learning in Materials Science ; Biomedical Text Mining and Ontologies ; Topic Modeling
Authors: Piia Liigand | Jaanus Liigand | Filip Cuyckens | Rob J. Vreeken | Anneli Kruve
Source: Analytica Chimica Acta (article, 2018-05-31)
Field: Chemistry — Spectroscopy

Ionisation efficiencies can be predicted in complicated biological matrices: A proof of concept Chemistry ; Ionization ; Analyte ; Electrospray ionization ; Abundance (ecology) ; Matrix (chemical analysis) ; Electrospray ; Analytical Chemistry (journal) ; Mass spectrometry ; Chromatography ; Ion ; Organic chemistry Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Analytical Chemistry and Chromatography
Authors: Mustafa Er | Abdulati Miftah Abounakhla | Hakan Tahtacı | Ali Hasin Bawah | Süleyman Selim Çınaroğlu | Abdurrahman Onaran | Abdulilah Ece
Source: Chemistry Central Journal (article, 2018-11-23)
Field: Chemistry — Organic Chemistry

An integrated approach towards the development of novel antifungal agents containing thiadiazole: synthesis and a combined similarity search, homology modelling, molecular dynamics and molecular docking study The overall results presented here might be a good starting point for the identification of novel and more active compounds as antifungal agents. Antifungal ; Chemistry ; Computational biology ; Molecular dynamics ; Docking (animal) ; Homology modeling ; Similarity (geometry) ; Structural similarity ; Combinatorial chemistry ; Biochemistry ; Computer science ; Computational chemistry ; Enzyme ; Biology ; Artificial intelligence ; Microbiology ; Medicine Synthesis and biological activity ; Synthesis and Biological Evaluation ; Click Chemistry and Applications
Authors: Xuehua Liu | Rue Yang | Mingcong Xu | Chunhui Ma | Wei Li | Yu Yin | Qiongtao Huang | Yiqiang Wu | Jian Li | Shouxin Liu
Source: Polymers (article, 2018-10-19)
Field: Materials Science — Biomaterials

Hydrothermal Synthesis of Cellulose Nanocrystal-Grafted-Acrylic Acid Aerogels with Superabsorbent Properties In this work, we applied a fast and simple method to synthesize cellulose nanocrystal (CNC) aerogels, via a hydrothermal strategy followed by freeze drying. The characteristics and morphology of the obtained CNC-g-AA aerogels were affected by the hydrothermal treatment time, volume of added AA (acrylic acid), and the mass fraction of the CNCs. The formation mechanism of the aerogels involved free radical graft copolymerization of AA and CNCs with the cross-linker N,N′-methylene bis(acrylamide) (MBA) during the hydrothermal process. The swelling ratio of the CNC-g-AA aerogels was as high as 495:1, which is considerably greater than that of other polysaccharide-g-AA aerogels systems. Moreover, the CNC-g-AA aerogels exhibited an excellent methyl blue (MB) adsorption capacity and the ability to undergo rapid desorption/regeneration. The maximum adsorption capacity of the CNC-g-AA aerogels for MB was greater than 400 mg/g. Excellent regeneration performance further indicates the promise of our CNC-g-AA aerogels as an adsorbent for applications in environmental remediation. Acrylic acid ; Hydrothermal circulation ; Nanocrystal ; Cellulose ; Materials science ; Aerogel ; Chemical engineering ; Hydrothermal synthesis ; Composite material ; Nanotechnology ; Copolymer ; Polymer Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Hydrogels: synthesis, properties, applications
Authors: Xiaojuan You | Enzhong Li | Jiayang Liu | Songhua Li
Source: Molecules (article, 2018-07-02)
Field: Chemistry — Analytical Chemistry

Using Natural Biomacromolecules for Adsorptive and Enzymatic Removal of Aniline Blue from Water The present study investigated the adsorptive and enzymatic removal of aniline blue dye (AB) from aqueous solution using waxy riceprocessing waste (RW), peanut shell (PS), microbial waste of <i>Aspergillus niger</i> (MW) as low cost adsorbents, and laccase (Lac) as a biocatalyst. Commercial activated carbon (AC) was also employed to compare the adsorption performance with the three adsorbents. Dye removal was examined under various parameters in batch experiments. It was found that dye removal by RW and Lac was 89⁻94% noticeably better than that by MW and PS (20⁻70%). In any cases, AC produced the highest dye removal among the tested materials. The kinetics, isotherms, and thermodynamics were then analyzed to elucidate the adsorption process by the four adsorbents. The pseudo-second order kinetic was superior to the pseudo first order kinetic model in describing adsorption for all adsorbents. The Langmuir model fitted the adsorption process very well, indicating monolayer coverage of dyes on a solid surface. A thermodynamic analysis of enthalpy (Δ<i>H</i>°), entropy (Δ<i>S</i>°), and Gibbs free energy (Δ<i>G</i>°) classified the adsorption as a nonspontaneous and endothermic process. The results reveal diverse natural materials (e.g., processing waste RW) as novel substitutes for traditional activated carbon, as well as laccase as a green catalyst for the treatment of dye wastewater. Chemistry ; Aniline ; Adsorption ; Aniline Compounds ; Chromatography ; Organic chemistry Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Nicolas Jouault | Sanat K. Kumar | R. Smalley | Changzai Chi | Robert Moneta | Barbara A. Wood | Holly L. S. Salerno | Yuri B. Melnichenko | Lilin He | W. E. Guise | Boualem Hammouda | Michael K. Crawford
Source: Macromolecules (article, 2018-07-11)
Field: Materials Science — Materials Chemistry

Do Very Small POSS Nanoparticles Perturb s-PMMA Chain Conformations? Small-angle neutron scattering (SANS) measurements of syndiotactic s-PMMA polymers mixed with weakly attractive 1.0 nm diameter polyhedral oligomeric silsesquioxane (POSS) nanoparticles (NPs) show no observable changes in the chain radius of gyration Rg, regardless of the polymer molecular weights, the amount of residual solvent, or the POSS NP loading and dispersion (from 0 to 20 vol %). In retrospect, these results are not surprising since scaling arguments imply that chain size in the concentrated region of the phase diagram of a polymer solution is ideal and independent of the polymer volume fraction ϕ, and only as the semidilute region is entered with decreasing concentration does the chain size for a good solvent begin to increase due to polymer excluded volume and then scales with concentration as ϕ–1/8. For typical polymer nanocomposites the NP concentrations are less than 50% v/v, so the polymers are still generally within the concentrated regions of their phase diagrams, where ideal chain conformations are observed for small molecule solvents. By combining the present results with previous results from the literature, we conclude that spherical NPs apparently have little effect on the conformations of polymer chains, especially in typical polymer nanocomposites that only incorporate moderate amounts of NPs. Polymer ; Radius of gyration ; Polymer chemistry ; Volume fraction ; Materials science ; Solvent ; Neutron scattering ; Excluded volume ; Polymer nanocomposite ; Small-angle neutron scattering ; Phase (matter) ; Hydrodynamic radius ; Nanoparticle ; Chemical engineering ; Chemistry ; Scattering ; Copolymer ; Organic chemistry ; Nanotechnology ; Composite material ; Physics Silicone and Siloxane Chemistry ; Polymer Nanocomposites and Properties ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Yoshiharu Nishiyama | Shirin Asaadi | Patrik Ahvenainen | Herbert Sixta
Source: Cellulose (article, 2018-12-01)
Field: Materials Science — Biomaterials

Water-induced crystallization and nano-scale spinodal decomposition of cellulose in NMMO and ionic liquid dope Small-angle X-ray scattering ; Materials science ; Spinodal decomposition ; Scattering ; Ionic liquid ; Cellulose ; Small-angle scattering ; Crystallization ; Diffusion ; Chemical engineering ; Analytical Chemistry (journal) ; Crystallography ; Chemistry ; Phase (matter) ; Chromatography ; Organic chemistry ; Thermodynamics ; Optics Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Polysaccharides and Plant Cell Walls
Authors: M.R. Ardigo-Besnard | Iolanda Valentina Popa | Sébastien Chevalier
Source: Corrosion Science (article, 2018-12-26)
Field: Materials Science — Materials Chemistry

Effect of spinel and perovskite coatings on the long term oxidation of a ferritic stainless steel in H2/H2O atmosphere Spinel ; Materials science ; Corrosion ; Isothermal process ; Oxide ; Coating ; Metallurgy ; Perovskite (structure) ; Layer (electronics) ; Evaporation ; Atmosphere (unit) ; Alloy ; Chromia ; Substrate (aquarium) ; Conversion coating ; Chemical engineering ; Composite material Advancements in Solid Oxide Fuel Cells ; High-Temperature Coating Behaviors ; Electronic and Structural Properties of Oxides
Authors: Miaoxia Xie | Xiang-Tao Shang | Lin‐Jie Zhang | Qing-Lin Bai | Tingting Xu
Source: Metals (article, 2018-03-04)
Field: Materials Science — Materials Chemistry

Interface Characteristic of Explosive-Welded and Hot-Rolled TA1/X65 Bimetallic Plate TA1/X65 bimetallic plate has a bright future of application by combining the excellent corrosion resistance of TA1 and the high strength of inexpensive X65 steel, while manufacturing large size TA1/X65 bimetallic plate is still a challenging task. Multi-pass hot-rolling is the most common way to achieve a large size bimetallic plate. In this work, interface characteristic of explosive-welded and multi-pass hot-rolled TA1/X65 bimetallic plate is experimentally studied. The microstructure, composition and microhardness distribution across the TA1/X65 interface are investigated by optical metallographic observation, scanning electron microscope (SEM) observation, energy dispersive spectrometer (EDS) analysis, and Vickers hardness test. Shear tests and stratified tensile tests are conducted with emphasis on impacts of the angle between loading direction and detonation wave propagation direction on interface strength. A straight TA1/X65 interface with periodic morphology of residual peninsula could be observed on the cross section parallel to detonation wave propagation direction, while in most cases there is no residual peninsula morphology on the straight TA1/X65 interface when the cross section is perpendicular to detonation wave propagation direction. TA1/X65 interface of explosive-welded and multi-pass hot-rolled bimetallic plate presents higher bearing capacity for the load perpendicular to detonation wave propagation direction than that for the load parallel to detonation wave propagation direction. The results of this paper have a certain guiding significance for the fabrication of pipes and containers made of explosive-welded and hot-rolled TA1/X65 bimetallic plate. Materials science ; Detonation ; Explosion welding ; Indentation hardness ; Microstructure ; Composite material ; Welding ; Bimetallic strip ; Optical microscope ; Scanning electron microscope ; Explosive material ; Metallurgy High-Velocity Impact and Material Behavior ; Advanced Welding Techniques Analysis ; Surface Treatment and Residual Stress
Authors: Litty V. Thekkekara | Xi Chen | Miṅ Gu
Source: Scientific Reports (article, 2018-07-31)
Field: Materials Science — Materials Chemistry

Two-photon-induced stretchable graphene supercapacitors Direct laser writing with an ultrashort laser beam pulses has emerged as a cost-effective single step technology for realizing high spatial resolution features of three-dimensional structures in confined footprints with potential for large area fabrication. Here we present the two-photon direct laser writing technology to develop high-performance stretchable biomimetic three-dimensional micro-supercapacitors with the fractal electrode distance down to 1 µm. With multilayered graphene oxide films, we show the charge transfer capability enhanced by order of 10<sup>2</sup> while the energy storage density exceeds the results in current lithium-ion batteries. The stretchability and the volumetric capacitance are increased to 150% and 86 mF/cm<sup>3</sup> (0.181 mF/cm<sup>2</sup>), respectively. This additive nanofabrication method is highly desirable for the development of self-sustainable stretchable energy storage integrated with wearable technologies. The flexible and stretchable energy storage with a high energy density opens the new opportunity for on-chip sensing, imaging, and monitoring. Supercapacitor ; Graphene ; Photon ; Nanotechnology ; Materials science ; Physics ; Capacitance ; Electrode ; Optics Graphene research and applications ; Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications