jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jakub Vrábel
Source: Brno University of Technology Digital Library (Brno University of Technology) (article, 2019-06-17)
Field: Chemistry — Analytical Chemistry

Popis Restricted Boltzmann machine metody ve vztahu se statistickou fyzikou a jeho následné využití ve zpracování spektroskopických dat Práca sa zaoberá spojeniami medzi štatistickou fyzikou a strojovým učením s dôrazom na základné princípy a ich dôsledky. Ďalej sa venuje obecným vlastnostiam spektroskopických dát a ich zohľadnení pri pokročilom spracovaní dát. Začiatok práce je venovaný odvodeniu partičnej sumy štatistického systému a štúdiu Isingovho modelu pomocou "mean field" prístupu. Následne, popri základnom úvode do strojového učenia, je ukázaná ekvivalencia medzi Isingovým modelom a Hopfieldovou sieťou - modelom strojového učenia. Na konci teoretickej časti je z Hopfieldovej siete odvodený model Restricted Boltzmann Machine (RBM). Vhodnosť použitia RBM na spracovanie spektroskopických dát je diskutovaná a preukázaná na znížení dimenzie týchto dát. Výsledky sú porovnané s bežne používanou Metódou Hlavných Komponent (PCA), spolu so zhodnotením prístupu a možnosťami ďalšieho zlepšovania. Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Image and Signal Denoising Methods
Authors: Kimberly To
Source: NCSU Libraries Repository (North Carolina State University Libraries) (article, 2019-03-19)
Field: Materials Science — Materials Chemistry

Predictive Modeling for Developmental Toxicity of Engineered Nanomaterials. Chapter 1 is a review of available nanotoxicity data and predictive modeling efforts through grouping, read across, and nano-QSAR.Chapter 2 describes a simulation study assessing data imputation methods for high throughput toxicological data and subsequent hazard ranking.Chapter 3 maps nanomaterial physicochemical features to multiple-endpoint developmental toxicity.Chapter 4 reviews the previous work and discusses future directions. Toxicity ; Computer science ; Risk analysis (engineering) ; Chemistry ; Business Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Frederik Heber | Zofia Trstanova | Benedict Leimkuhler
Source:  (preprint, 2019-03-20)
Field: Materials Science — Materials Chemistry

TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications With the advent of GPU-assisted hardware and maturing high-efficiency software platforms such as TensorFlow and PyTorch, Bayesian posterior sampling for neural networks becomes plausible. In this article we discuss Bayesian parametrization in machine learning based on Markov Chain Monte Carlo methods, specifically discretized stochastic differential equations such as Langevin dynamics and extended system methods in which an ensemble of walkers is employed to enhance sampling. We provide a glimpse of the potential of the sampling-intensive approach by studying (and visualizing) the loss landscape of a neural network applied to the MNIST data set. Moreover, we investigate how the sampling efficiency itself can be significantly enhanced through an ensemble quasi-Newton preconditioning method. This article accompanies the release of a new TensorFlow software package, the Thermodynamic Analytics ToolkIt, which is used in the computational experiments. Analytics ; Computer science ; Software ; Artificial intelligence ; Sampling (signal processing) ; Machine learning ; Data science ; Programming language Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Stochastic Gradient Optimization Techniques
Authors: Jun Dai | Roman V. Krems
Source: arXiv (Cornell University) (preprint, 2019-07-19)
Field: Materials Science — Materials Chemistry

Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels Gaussian process regression has recently emerged as a powerful, system-agnostic tool for building global potential energy surfaces (PES) of polyatomic molecules. While the accuracy of GP models of PES increases with the number of potential energy points, so does the numerical difficulty of training and evaluating GP models. Here, we demonstrate an approach to improve the accuracy of global PES without increasing the number of energy points. The present work reports four important results. First, we show that the selection of the best kernel function for GP models of PES can be automated using the Bayesian information criterion as a model selection metric. Second, we demonstrate that GP models of PES trained by a small number of energy points can be significantly improved by iteratively increasing the complexity of GP kernels. The composite kernels thus obtained maximize the accuracy of GP models for a given distribution of potential energy points. Third, we show that the accuracy of the GP models of PES with composite kernels can be further improved by varying the training point distributions. Fourth, we show that GP models with composite kernels can be used for physical extrapolation of PES. We illustrate the approach by constructing the six-dimensional PES for H$_3$O$^+$. For the interpolation problem, we show that this algorithm produces a global six-dimensional PES for H$_3$O$^+$ in the energy range between zero and $21,000$ cm$^{-1}$ with the root mean square error $65.8$ cm$^{-1}$ using only 500 randomly selected {\it ab initio} points as input. To illustrate extrapolation, we produce the PES at high energies using the energy points at low energies as input. We show that one can obtain an accurate global fit of the PES extending to $21,000$ cm$^{-1}$ based on 1500 potential energy points at energies below $10,000$ cm$^{-1}$. Extrapolation ; Kriging ; Gaussian process ; Gaussian ; Interpolation (computer graphics) ; Kernel (algebra) ; Potential energy ; Energy (signal processing) ; Range (aeronautics) ; Mathematics ; Computer science ; Mathematical optimization ; Applied mathematics ; Algorithm ; Artificial intelligence ; Statistics ; Physics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mehrdad Yousefi
Source: Preprints.org (preprint, 2019-06-27)
Field: Materials Science — Materials Chemistry

Large-Scale Multiscale Modeling of Phase Transformation in Nanocrystalline Materials: Atomistic and Phase-Field Methods In this research, atomistic molecular dynamics simulations are combined with mesoscopic phase-field computational methods in order to investigate phase-transformation in polycrystalline Aluminum microstructure. In fact, microstructural computational modeling of engineering materials could help to optimize their mechanical properties for industrial applications (e.g. directional solidification for turbine blades). As a result, a multiscale modeling approach is developed to find a relation between manufacturing variables (e.g. temperature) and microstructural properties of crystalline materials (e.g. grain size), which could be used to develop an advanced manufacturing process for sensitive applications. The results show that atomistic modeling of grain growth could be used as a first-principle approach in order to study phase transformation's kinetics, which could capture morphology of polycrystalline materials more accurately. On the other hand, phase-field mesoscopic approach needs less computational efforts, but still it relies on semi-empirical data to capture accurate phase transformation regimes, which makes this approach suitable for rapid examining of new manufacturing conditions as well as its effects on microstructural properties of polycrystalline materials. Mesoscopic physics ; Materials science ; Nanocrystalline material ; Crystallite ; Phase (matter) ; Multiscale modeling ; Microstructure ; Transformation (genetics) ; Molecular dynamics ; Grain size ; Nanotechnology ; Mechanical engineering ; Metallurgy ; Engineering ; Computational chemistry ; Physics Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Solidification and crystal growth phenomena
Authors: István Rigó | M. Vereš | L. Himics | Zsuzsanna Pápa | Orsolya Hakkel | Péter Fürjes
Source: Preprints.org (preprint, 2019-09-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancement in Inverse Pyramid SERS Substrates with Entrapped Gold Nanoparticles Giant plasmonic surface enhancement has been observed in gold coated micron sized inverse pyramids entrapping a gold nanoparticle. The amplification of both surface enhanced Raman and photoluminescence signals was found to be dependent on the diameter of trapped gold nanoparticle and around 50-fold enhancement was detected for 250nm diameter sample relatively to the 50nm one. Finite differential time domain simulations, performed to determine the near-field distribution in the structure, showed that when the nanoparticle protrudes into the hotspot zone of the void, coupling of electromagnetic field occurs and the plasmon-related near-field enhancement is concentrated into the close vicinity of the nanoparticle, mainly into the close gaps around the tangential points of the curved sphere and the flat pyramid surface. This results in a more than 15 times increase of the near-field intensity, compared to the empty void. Materials science ; Nanoparticle ; Plasmon ; Surface plasmon ; Colloidal gold ; Raman spectroscopy ; Void (composites) ; Photoluminescence ; Nanotechnology ; Near and far field ; Optics ; Molecular physics ; Optoelectronics ; Composite material ; Chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Near-Field Optical Microscopy
Authors: Sadid Muneer
Source: OpenCommons at University of Connecticut (University of Connecticut) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

High Temperature Non-equilibrium Non-isothermal Semiconductor Modeling Under extreme thermal gradients (~&gt; 1 K/nm) significant asymmetric melting of current-carrying highly-doped silicon micro-wires was observed. This bizarre phenomenon was explained by the generation-transport-recombination (GTR) process of the minority carriers in semiconductors at elevated temperatures. The finite element simulation model, using effective media approximation, supported this explanation; however, the asymmetry observed in the experiments is significantly larger than that obtained from the computational results. As the calculation based on conventional non-isothermal semiconductor physics underestimates the exponential growth of carrier generation near melting, the carrier concentration, hence the shift of the hottest spot because of the thermoelectric Thomson effect, was underestimated.\nIn this work, a high temperature semiconductor model is developed where the continuity equations for electrons and holes are solved and the energy exchange associated with generation-recombination processes are duly accounted for. Modified generation-recombination mechanisms included in the model sharply increase the carrier concentration to ~1022 cm-3 at melting for silicon, and the shift of hottest spot is closer to that observed in the experiments. This approach allows us to calculate a more accurate melt profile in non-equilibrium conditions, which is also critically important for designing phase change memory (PCM) cells. PCM is an emerging non-volatile memory technology where electrical pulses are used to transition between high and low resistance states and where the thermoelectric effects are significant due to extreme thermal gradients (~&gt; 50 K/nm) at high operating temperatures.\nTo adopt this model for PCM, characterization of material properties at elevated temperature is required. In this work, the effective activation energy of metastable Ge2Sb2Te5 (GST) is calculated from device level high speed resistivity measurements data. The extracted temperature dependent activation energy shows parabolic shape with maximum at ~450 K, and is expected to correspond to a distribution of trap levels that contribute to conduction. Also, from the simultaneous Seebeck-temperature and resistivity-temperature measurements of amorphous-fcc (face centered cubic) mixed-phase GST thin-films, the Fermi energy of single crystal fcc GST was calculated, which is 0.16 eV bellow the valence band edge. Isothermal process ; Thermodynamics ; Materials science ; Physics Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design
Authors: Bradly Alicea | Richard Gordon | Thomas Harbich | U. Singh | Asmit Kumar Singh | Vinay Varma
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-12-23)
Field: Materials Science — Biomaterials

Towards a digital diatom: image processing and deep learning analysis of <i>Bacillaria paradoxa</i> dynamic morphology Abstract Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa , an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa , we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria . Computer science ; Artificial intelligence ; Diatom ; Variety (cybernetics) ; Function (biology) ; Deep learning ; Convergence (economics) ; Convergent evolution ; Machine learning ; Biology ; Evolutionary biology ; Ecology Diatoms and Algae Research ; Cell Image Analysis Techniques ; Digital Imaging for Blood Diseases
Authors: Musa Büyükada
Source: Pamukkale University Journal of Engineering Sciences (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Removal of COD and TOC from various model textile dyes by ozonation: Box-Behnken approach Bu almada bir ileri oksidasyon sreci olan ozonlama kullanlarak eitli sentetik atk sularndan KO ve TOK giderimleri incelenmitir. Deneylerin planlanmasnda bir tr cevap yzey metodu olan Box Behnken tasarmndan (BBT) yararlanlmtr. letme parametreleri olarak ozon dozu (OD, mg/L), reaksiyon sresi (RS, dk) ve balang boya konsantrasyonu (BBK, mg/L) belirlenmitir. %90'n zerinde KO ve %80'in zerinde TOK giderimi, ozonlamann sentetik atk sularn artmnda etkili bir sre tipi olduunu gstermitir. Son olarak oklu dorusal olmayan regresyon (DOR) ile modelleme, ilaveten optimizasyon ile validasyon almalar gerekletirilmitir. Tretilen DOR modellerinin yant deikenlerini tahmin edebilme gc %99 olarak belirlenmitir. 24 mg/L OD, 40 dk RS ve 10 mg/L BBK artlar BBT-optimize iletme artlar olarak belirlenmitir. Bu artlarda tm sentetik atk sulardan yaklak %95 KO ve %85 TOK giderimi ngrlmtr. Optimize artlardaki validasyon deneylerinde elde edilen sonular bu tahminsel deerleri dorulamtr. Tahminsel ve deneysel sonular arasndaki byk uyum BBT'nin optimizasyondaki doruluunu ve baarsn Box–Behnken design ; Textile ; Pulp and paper industry ; Chemistry ; Chromatography ; Materials science ; Engineering ; Response surface methodology ; Composite material Dye analysis and toxicity ; Dyeing and Modifying Textile Fibers
Authors: Christian Wülker | Sipu Ruan | Gregory S. Chirikjian
Source: Research (preprint, 2019-01-01)
Field: Materials Science — Materials Chemistry

Quantizing Euclidean Motions via Double-Coset Decomposition Concepts from mathematical crystallography and group theory are used here to quantize the group of rigid-body motions, resulting in a "motion alphabet" with which robot motion primitives are expressed. From these primitives it is possible to develop a dictionary of physical actions. Equipped with an alphabet of the sort developed here, intelligent actions of robots in the world can be approximated with finite sequences of characters, thereby forming the foundation of a language in which robot motion is articulated. In particular, we use the discrete handedness-preserving symmetries of macromolecular crystals (known in mathematical crystallography as Sohncke space groups) to form a coarse discretization of the space SE(3) of rigid-body motions. This discretization is made finer by subdividing using the concept of double-coset decomposition. More specifically, a very efficient, equivolumetric quantization of spatial motion can be defined using the group-theoretic concept of a double-coset decomposition of the form Γ\SE(3)/Δ, where Γ is a Sohncke space group and Δ is a finite group of rotational symmetries such as those of the icosahedron. The resulting discrete alphabet is based on a very uniform sampling of SE(3) and is a tool for describing the continuous trajectories of robots and humans. An efficient coarse-to-fine search algorithm is presented to round off any motion sampled from the continuous group of motions to the nearest element of our alphabet. It is shown that our alphabet and this efficient rounding algorithm can be used as a geometric data structure to accelerate the performance of other sampling schemes designed for desirable dispersion or discrepancy properties. Moreover, the general "signals to symbols" problem in artificial intelligence is cast in this framework for robots moving continuously in the world. Euclidean group ; Group (periodic table) ; Rigid body ; Discretization ; Homogeneous space ; Mathematics ; Symmetry group ; Motion (physics) ; Euclidean space ; Coset ; Lie group ; Discrete space ; Pure mathematics ; Combinatorics ; Computer science ; Artificial intelligence ; Mathematical analysis ; Geometry ; Physics ; Classical mechanics ; Quantum mechanics Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; RNA and protein synthesis mechanisms
Authors: Muhammad Zubair | Stefanie Sandlöbes-Haut | Max A. Wollenweber | Katrin Bugelnig | Carl F. Kusche | Guillermo Requena | Sandra Korte‐Kerzel
Source: arXiv (Cornell University) (preprint, 2019-08-22)
Field: Materials Science — Biomaterials

Strain heterogeneity and micro-damage nucleation under tensile stresses\n in an Mg-5Al-3Ca alloy with an intermetallic skeleton Strain heterogeneity at the microstructural level plays a vital role in the\ndeformation and fracture behaviour of dual or multi-phase materials. In the\npresent work, the strain heterogeneity, localization and partitioning arising\nat the sub-micron scale during elevated temperature (170 {\\deg}C) tensile\ndeformation of an Mg-5Al-3Ca alloy was investigated using quasi in-situ\n{\\mu}-DIC experiments. The results reveal that the strain is mainly carried by\nthe {\\alpha}-Mg phase, while the intermetallic Laves phase plays a critical\nrole in that strain concentrations build up at the {\\alpha}-Mg matrix and Laves\nphase interfaces, hence, reducing the overall deformability of the alloy. In\nquasi in-situ and bulk material analysis at elevated temperature, cracks were\nobserved to nucleate in the Laves phase, at i) the intersection points of slip\nlines in the {\\alpha}-Mg matrix with the Laves phase and ii) the twin\nintersections with {\\alpha}-Mg/Laves phase interfaces and iii) twin\ntransmissions across {\\alpha}-Mg/Laves phase interfaces. Euler number analysis\nhas shown that the (inter-)connectivity of the Laves phase decreases with\ndeformation. Finally, cracks grow preferentially along the Laves phases until\nthe material fractures.\n Laves phase ; Intermetallic ; Materials science ; Alloy ; Nucleation ; Ultimate tensile strength ; Phase (matter) ; Deformation (meteorology) ; Metallurgy ; Crystallography ; Composite material ; Thermodynamics ; Chemistry Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Liwei Cao | Danilo Russo | Vassilios S. Vassiliadis | Alexei A. Lapkin
Source: ChemRxiv (preprint, 2019-07-03)
Field: Materials Science — Materials Chemistry

Identifying Physico-Chemical Laws from the Robotically Collected Data A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models. Computer science ; Physical law ; Symbolic regression ; Experimental data ; Nonlinear system ; Focus (optics) ; Work (physics) ; Variable (mathematics) ; Mathematical optimization ; Applied mathematics ; Algorithm ; Mathematics ; Machine learning ; Statistics ; Genetic programming ; Engineering ; Mechanical engineering ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Fault Detection and Control Systems ; Protein Structure and Dynamics
Authors: Alexander V. Malozemov
Source: Thermal Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Investigating permeability of porous graphene A graphene sheet with regular pores similar to the surface form of boron nitride is simulated be means of crystallographic constructions. The sheet is represented by the aggregate of carbon atoms in the corresponding positions of the crystal structure which do not experience thermal oscillations. Within the framework of the presented approximation, permeability of porous graphene with respect to natural gas components is theoretically analysed. Based on the results obtained, selectivity of separation for methane-helium mixtures is determined. Graphene ; Materials science ; Boron nitride ; Methane ; Porosity ; Helium ; Permeability (electromagnetism) ; Porous medium ; Boron ; Chemical physics ; Carbon fibers ; Selectivity ; Chemical engineering ; Composite material ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite number Carbon Nanotubes in Composites ; Graphene research and applications ; Gas Dynamics and Kinetic Theory