jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yashasvi S. Ranawat
Source: Aaltodoc (Aalto University) (article, 2018-11-06)
Field: Materials Science — Materials Chemistry

Descriptor based adsorption-energy prediction on nano-clusters for catalyst selection Nano-catalyst design, supplanting critical/rare metals with earth-abundant elements, for hydrogen evolution reactions (HERs) is a significant material-science and economic challenge. These design challenges can be significantly overcome by extensive first principle simulations. However, these simulations are computationally costly. With an introduction of descriptor, machine-learning methods afford significant advantages in such scenario. Descriptors: Smooth Overlap of Atomic Positions (SOAP) based on charge density, BLEACH, and Local Many- Body Tensor Representation (LMBTR) are proposed, and developed. These are evaluated on database of AuCu, and MoS2 nano-clusters. A learning error of 0.05 eV in adsorption energy, and 1.7 me in charge on hydrogen are realised for LMBTR; while 0.08 eV and 13.4 me, respectively for BLEACH on the AuCu dataset. Although not as accurate as state-of-the-art SOAP-lite (3.36 meV and 0.077 me, respectively), these descriptors have their own benefits. While LMBTR allows for bi-directional operability, BLEACH provides element-agnosticism; both of which missing are from SOAP. Catalysis ; Selection (genetic algorithm) ; Adsorption ; Computer science ; Chemistry ; Artificial intelligence ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Chang Rajani
Source: Työväentutkimus Vuosikirja (article, 2018-01-01)
Field: Materials Science — Biomaterials

Semi-supervised detection of industrial fouling using ultrasound Fouling is a large scale problem in industrial equipment such as heat exchangers or pipes, used in factories, ships, airplanes, etc. Traditionally, such equipment is cleaned using sandblasting, chemicals or mechanical methods, all of which require halting the process, which is costly.&#13;\n&#13;\nRecently, high-power ultrasound has become a viable option to these methods. In ultrasonic cleaning ultrasound is projected into the equipment from the outside, which means that the equipment does not need to be halted to perform cleaning. While the cleaning itself is not invasive in nature, in most cases vision cannot be used to determine whether cleaning is actually necessary or not. What remains is to have such a method that is also non-invasive.&#13;\n&#13;\nIt is possible to use ultrasound as a kind of a radar to detect whether or not fouling is present, and this has been attempted in previous literature. However, until now, such methods have required extensive manual calculation and knowledge of the physical properties of the setup.&#13;\n&#13;\nWe present the first ever system to concurrently clean and detect industrial fouling using ultrasound and deep learning. Our method does not rely on specific properties of the equipment, allowing it to generalize to large industrial processes where it is not practical to calculate or simulate the cleaning scenario. To this end, we extend existing literature on semi-supervised learning by presenting algorithms used to learn from a monotonic process, and model the high-dimensional signal data using a convolutional neural network that is highly robust to temporal variance. This thesis presents the machine learning solution behind the system, and the cleaning components are provided by&#13;\nAltum Technologies.&#13;\n&#13;\nFurther, we explore methods to detect and counter the so-called domain shift that occurs when experimenting in the physical world, and provide experimental evidence that our methods work in practice. Fouling ; Environmental science ; Artificial intelligence ; Computer science ; Chemistry Calcium Carbonate Crystallization and Inhibition
Authors: Mohammad M. Sultan | Vijay S. Pande
Source: arXiv (Cornell University) (preprint, 2018-02-28)
Field: Materials Science — Materials Chemistry

Automated design of collective variables using supervised machine\n learning Selection of appropriate collective variables for enhancing sampling of\nmolecular simulations remains an unsolved problem in computational biophysics.\nIn particular, picking initial collective variables (CVs) is particularly\nchallenging in higher dimensions. Which atomic coordinates or transforms there\nof from a list of thousands should one pick for enhanced sampling runs? How\ndoes a modeler even begin to pick starting coordinates for investigation? This\nremains true even in the case of simple two state systems and only increases in\ndifficulty for multi-state systems. In this work, we solve the initial CV\nproblem using a data-driven approach inspired by the filed of supervised\nmachine learning. In particular, we show how the decision functions in\nsupervised machine learning (SML) algorithms can be used as initial CVs\n(SML_cv) for accelerated sampling. Using solvated alanine dipeptide and\nChignolin mini-protein as our test cases, we illustrate how the distance to the\nSupport Vector Machines' decision hyperplane, the output probability estimates\nfrom Logistic Regression, the outputs from deep neural network classifiers, and\nother classifiers may be used to reversibly sample slow structural transitions.\nWe discuss the utility of other SML algorithms that might be useful for\nidentifying CVs for accelerating molecular simulations.\n Artificial intelligence ; Machine learning ; Hyperplane ; Computer science ; Support vector machine ; Sampling (signal processing) ; Artificial neural network ; Supervised learning ; Sample (material) ; Variable (mathematics) ; Algorithm ; Mathematics ; Detector Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Andy Brunning﻿﻿
Source: C&EN Global Enterprise (article, 2018-12-17)
Field: Chemistry — Physical and Theoretical Chemistry

Periodic graphics: Top C&amp;EN stories in 2018 Chemical educator and Compound Interest blogger Andy Brunning recounts our most-read news and feature stories of the year﻿ To download a pdf of this article, visit http://cenm.ag/9649cover11. References used to create this graphic:New definitions for the kilogram and moleSTING fever is sweeping through the cancer immunotherapy worldNerve agent attack on spy used ‘Novichok’ poisonC&EN’s global top 50 chemical companiesIs machine learning overhyped?Charges dropped against UCLA chemistry professor Patrick Harran for death of Sheri Sangji after lab fire A collaboration between C&EN and Andy Brunning, author of the popular graphics blog Compound Interest To see more of Brunning’s work, go to compoundchem.com. To see all of C&EN’s Periodic Graphics, visit http://cenm.ag/periodicgraphics.﻿ Graphics ; Computer graphics (images) ; Computer science History and advancements in chemistry ; Chemistry and Chemical Engineering ; Academic Writing and Publishing
Authors: Dane Morgan | Alan Dozier | Raphael A. Finkel | Gerbrand Ceder | Kristin Persson
Source: Open MIND (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

SI2-SSI Collaborative Research: A Computational Materials Data and Design Environment This poster describes results associated with a project for the National Science Foundation, grant # 1148011. It was prepared for a PIs meeting on 2018-04-30. The primary results are •The Materials Simulation Toolkit (MAST) for high-throughput defect and diffusion modeling •A Machine Learning extension (MAST-ML) to rapidly generate machine learning models from materials data. •Online defect and diffusion analysis apps on MaterialsHub. •The world’s largest computed and machine learning enhanced diffusion database with easy online search. •Valuable research results using these tools and data, e.g. new fuel cell materials. •Workforce training through the Informatics Skunkworks, an undergraduate materials informatics group. Informatics ; Computer science ; Materials informatics ; Workforce ; Data science ; Health informatics ; Engineering ; Engineering informatics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Fredrik Eriksson | Erik Fransson | Paul Erhart
Source: arXiv (Cornell University) (preprint, 2018-11-22)
Field: Materials Science — Materials Chemistry

Efficient extraction of high-order force constants by machine learning using the hiphive package. The efficient extraction of force constants (FCs) is crucial for the analysis of many thermodynamic materials properties. Approaches based on the systematic enumeration of finite differences scale poorly with system size and can rarely extend beyond third order when input data is obtained from first-principles calculations. Methods based on parameter fitting in the spirit of interatomic potentials, on the other hand, can extract FC parameters from semi-random configurations of high information density and advanced regularized regression methods can recover physical solutions from a limited amount of data. Here, we present the hiPhive Python package, that enables the construction of force constant models up to arbitrary order. hiPhive exploits crystal symmetries to reduce the number of free parameters and then employs advanced machine learning algorithms to extract the force constants. Depending on the problem at hand both over and underdetermined systems are handled efficiently. The FCs can be subsequently analyzed directly and or be used to carry out e.g., molecular dynamics simulations. The utility of this approach is demonstrated via several examples including ideal and defective monolayers of MoS$_2$ as well as bulk nickel. Python (programming language) ; Underdetermined system ; Computer science ; Computational science ; Statistical physics ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Evgeny Blokhin | P. Villars
Source: arXiv (Cornell University) (preprint, 2018-06-09)
Field: Materials Science — Materials Chemistry

Quantitative trends in 8 physical properties of 115000 inorganic compounds gained by machine learning We applied the decision trees (random forest) machine-learning technique for the large experimental materials dataset PAULING FILE, compiled from the world's peer-reviewed literature. The training and validation data were extracted from the hundreds of thousands of publications in materials science (1891-2017). Then, for the nearly 115'000 distinct inorganic compounds we predicted 8 thermodynamic, mechanical, and electronic properties, using the only crystalline structures as an input. For the predicted physical properties we observed certain periodical patterns in all unary, binary, ternary, and quaternary compounds. We also solved a reversed task of predicting the possible crystalline structure based on a given combination of values of the 8 mentioned properties. Therefore our observations may play a role of the periodic table, formulated not for the chemical elements, but for the entire set of materials. Unary operation ; Ternary operation ; Periodic table ; Binary number ; Task (project management) ; Set (abstract data type) ; Computer science ; Random forest ; Table (database) ; Machine learning ; Materials science ; Artificial intelligence ; Data mining ; Chemistry ; Mathematics ; Arithmetic ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Biblik, Irina V. | Avramov, Konstantin V. | Rusanov, Roman A.
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2018-10-11)
Field: Materials Science — Materials Chemistry

Прогнозування ерозійно-корозійного зносу елементів трубопроводів атомних електростанцій методом нейромережевого моделювання Based on a comprehensive approach that uses the computer simulation of the process of destroying structural materials and technology of self-learning neural networks, a methodology has been developed for predicting the rate of flow accelerated corrosion (FAC) of pipeline elements with a single-phase medium of the second circuit of nuclear power plants (NPPs). The neural network model has been implemented in the Delphi Integrated Development Environment. The neural network consists of an input layer containing seven elements and an output layer with two elements. As the input variables of the neural network, the parameters that have the greatest influence on FAC process are chosen. These are the medium temperature, the pipeline internal diameter, the oxygen content in the medium, the coolant flow velocity, the hydrogen index, the time of monitoring (or the start of operation), and the time for which the prediction is performed. For each of the network input parameters, intervals of possible values were chosen. At that, the factors that affect FAC rate, but not included in the feasible model (chromium, copper and molybdenum content in the pipeline material, amine type) are assumed to be permanent. As the output parameters of the neural network, FAC rate and the variation of the pipeline element wall thickness within the predicted time interval have been selected. As the activation function of the neural network the sigmoid function is used. As a method of training the neural network, the error back propagation method has been chosen, which assumes both a forward and reverse passage through the network layers. As the learning algorithm of the neural network, the one with a teacher has been chosen. As a test sample for the neural network, it is proposed, along with operational control data, to use the results of calculations based on a statistical model created in the framework of a special calculation-experimental method. The application of the developed methodology makes it possible to improve the prediction accuracy of FAC rate without determining all the dependencies between the many factors that influence FAC process. The low errors of the constructed models make it possible to use the results of calculations both to determine the resource characteristics of NPP pipelines and optimize operational control. Artificial neural network ; Pipeline (software) ; Sigmoid function ; Pipeline transport ; Volumetric flow rate ; Interval (graph theory) ; Process (computing) ; Backpropagation Material Properties and Failure Mechanisms ; Environmental and Industrial Safety ; Nuclear Engineering Thermal-Hydraulics
Authors: George Alexandru Nemneş | T. L. Mitran | Andrei Manolescu
Source: arXiv (Cornell University) (preprint, 2018-12-11)
Field: Materials Science — Materials Chemistry

Gap prediction in hybrid graphene - hexagonal boron nitride nanoflakes using artificial neural networks The electronic properties graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined {\it ab initio} density functional theory calculations and machine learning techniques. The energy gaps of the quasi-0D graphene based systems, defined as the differences between LUMO and HOMO energies, depend on the sizes of the hBN domains relative to the size of the pristine graphene nanoflake, but also on the position of the hBN domain. The range of the energy gaps for different configurations is increasing as the hBN domains get larger. We develop two artificial neural network (ANN) models able to reproduce the gap energies with high accuracies and investigate the tunability of the energy gap, by considering a set of GNFs with embedded rectangular hBN domains. In one ANN model, the input is in one-to-one correspondence with the atoms in the GNF, while in the second model the inputs account for basic structures in the GNF, allowing potential use in up-scaled structures. We perform a statistical analysis over different configurations of ANNs to optimize the network structure. The trained ANNs provide a correlation between the atomic system configuration and the magnitude of the energy gaps, which may be regarded as an efficient tool for optimizing the design of nanostructured graphene based materials for specific electronic properties. Graphene ; Materials science ; Artificial neural network ; Band gap ; Ab initio ; Hexagonal boron nitride ; Nanotechnology ; Hybrid functional ; Density functional theory ; Boron nitride ; Position (finance) ; Electronic structure ; Topology (electrical circuits) ; Computer science ; Artificial intelligence ; Computational chemistry ; Optoelectronics ; Condensed matter physics ; Physics ; Mathematics ; Quantum mechanics ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: James M. LeBeau
Source: cIRcle (University of British Columbia) (article, 2018-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Quantifying local structure and chemistry with the scanning transmission electron microscope In his talk, J. LeBeau presented his work improving both the accuracy and precision of STEM imaging. While electron microscopy has been revolutionized by the aberration corrector, which dramatically improved spatial resolution, real-space distance measurements have remained semi-quantitative. In particular, accuracy and precision for scanning transmission electron microscopy (STEM) was significantly hampered by the presence of sample drift and scan distortion. Until recently, this limitation has obscured the capabilities to characterize minute changes to the atomic structure that can ultimately define material properties. He discussed his approach to resolve this problem, called revolving scanning transmission electron microscopy (RevSTEM). The method uses a series of fast-acquisition STEM images, but with the scan coordinates rotated between successive frames, which encodes drift rate and direction to the resulting image distortion. Multiple case studies were presented to highlight the power of this new technique to characterize materials. For example, picometer precise measurements were shown to enable the direct quantification of static atomic displacements within a complex oxide solid solution due to local chemistry. He also discussed recent work implementing deep convolutional neural networks to autonomously quantify electron diffraction data. The networks were shown to first calibrate the zero-order disk size, center position, and rotation without the need for pretreating the data for sample thickness and tilt measurements. The performance of the network was explored as a function of a variety of variables including thickness, tilt, and dose. The processing speed was also shown to far outpace a least squares approach by orders of magnitude. He also discussed the generality of the method to other materials/orientations as well as a hybrid approach that combines the features of the neural network with least squares fitting for even more robust analysis. Conventional transmission electron microscope ; Scanning transmission electron microscopy ; Transmission electron microscopy ; Scanning electron microscope ; Electron microscope ; Microscope ; Scanning confocal electron microscopy ; Chemistry ; Environmental scanning electron microscope ; Nanotechnology ; Materials science ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques
Authors: Humberto González‐Díaz | Sonia Arrasate | Nuria Sotomayor | Esther Lete | Fernando P. Cossío | Esther Domı́nguez | Danail Bonchev | Subhash C. Basak | David Quesada | Aliuska Duardo-Sánchez | James J. Chou | Carlos Romeo-Casabona
Source:  (article, 2018-04-09)
Field: Materials Science — Materials Chemistry

&lt;p&gt;MOL2NET: FROM MOLECULES TO NETWORKS (PROC. BOOK), 2018, Vol. 1, 761 pp.&lt;/p&gt; We are glad to invite all colleagues worldwide to participate on a new edition of this International Conference Series. The official title of this conference series is MOL2NET International Conference Series on Multidisciplinary Sciences. MOL2NET (the conference running title) is the acronym of the lemma of the conference: From Molecules to Networks. This running title is inspired by the possibility of multidisciplinary collaborations in science. The topics of interest include, but are not limited to, Chemistry (All areas), Mathematics (Applied), Physics (Applied), Materials Science, Nanotechnology, Biology and Life Sciences (All areas), Medicine, Biomedical Engineering, Education, along with Computer Sciences, Data Analysis, Statistics, Artificial Intelligence, Deep Learning, Bioinformatics, Systems Biology, and Complex Networks Sciences. See the following note to authors on topics outside the scope of the conference and associated workshops . The Scientific Headquarters (HQs) of this conference series are in the Faculty of Science and Technology, University of Basque Country (UPV/EHU), Biscay. However, the idea of this multidisciplinary conference emerged from the melting pot formed as the result of multiple collaborations of professors from many centers worldwide. Locally, the founders and strongest supporters of the conference are professors endowed by IKERBASQUE, Basque Foundation for Sciences, professors from the two departments Department of Organic Chemistry I and Department of Organic Chemistry II of the University of Basque Country (UPV/EHU), and professors from the Department of Computer Sciences of the University of Coru&ntilde;a (UDC). In addition, professors / researchers from the Center for the Study of Biological Complexity of the Virginia Commonwealth University (VCU), USA, the Natural Resources Research Institute, of the University of Minnesota, USA, and many other institutions are also founders and supporters of this conference, please see full committees lists. Computer science Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Health, Environment, Cognitive Aging
Authors: Eitam Vinegrad | Uri Hananel | Gil Markovich | Ori Cheshnovsky
Source: arXiv (Cornell University) (preprint, 2018-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Determination of Handedness in a Single Chiral Nanocrystal via Circularly Polarized Luminescence The occurrence of biological homochirality is attributed to symmetry breaking mechanisms which are still debatable1. Studies of symmetry breaking require tools for monitoring the population ratios of individual chiral nano-objects, such as molecules, polymers or nanocrystals. Moreover, mapping their spatial distributions may elucidate on the symmetry breaking mechanism. Recently, researchers have utilized differential scattering or circular dichroism microscopy to identify chirality on individual plasmonic nanostructures and inorganic nanocrystals. However, these measurements are prone to optical system artifacts. While luminescence is preferred for detecting single particle chirality, the typical low optical activity of chromophores limits its applicability. Here, we report on handedness determination of single chiral lanthanide based nanocrystals, using circularly polarized luminescence, with total photon count of $2\times 10^4$. We also utilize a machine learning approach for correlative microscopy to determine and spatially map handedness of individual nanocrystals with high accuracy. This technique may become invaluable in studies of spontaneous symmetry breaking in chiral materials. Luminescence ; Chirality (physics) ; Symmetry breaking ; Homochirality ; Nanocrystal ; Materials science ; Circular dichroism ; Chromophore ; Chemical physics ; Chiral symmetry breaking ; Scattering ; Molecular physics ; Population ; Nanotechnology ; Crystallography ; Optoelectronics ; Physics ; Optics ; Chemistry ; Photochemistry ; Quantum mechanics ; Stereochemistry ; Enantiomer Metamaterials and Metasurfaces Applications ; Photoreceptor and optogenetics research