jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Srinivas | L. Gomathi Devi | E Borgarello | J Kiwi | M Gratzel | E Pelizzetti | M Visca | H Yamashita | Y Ichihashi | M Takeuchi | S Kishiguchi | M Anpo | M Valenzuela | P Bosch | J Jimenez-Becerrill | O Quiroz | A I Paez | H Borse | J Jang | S J Hong | Lee Jung | J H Hong | T Jeong | E Hong | K S Yoon | J Kim | H G | H Yang | J Z Lu | X Cheng | Y Tang | M Matzapetakis | N Karligiano | A Bino | M Dakanali | P Raptopoulou | V Angoulis | A Terzis | J Giapintzakis | Salifoglou | R Dom | R Subasri | K Radha | S Noppakun | C Christopher | Cuie Berndt | James | Gomathi Devi | L | Srinivas | E Engenidou | K Fytianos | I Poulios
Source: Chemical Science Transactions (article, 2019-04-07)
Field: Chemistry — Inorganic Chemistry

The Enhanced Photocatalytic Activity of Mnx Co1-x Fe2 O4 Over the Znx Co1-x Fe2 O4 Under the Visible Light Irradiation: A Comparative Study Zn x Co 1-x Fe 2 O 4 and Mn x Co 1-x Fe 2 O 4 (where x=0.5 atom %) catalysts were synthesized by sol-gel technique and were further characterized by various analytical techniques. PXRD pattern confirms cubic spinel structure for both the samples. UV-Visible spectra show extended absorption in the visible region and the band gap values of Zn 0.5 Co 0.5 Fe 2 O 4 and Mn 0.5 Co 0.5 Fe 2 O 4 catalysts were found to be 1.71 and 1.67 eV. Photocatalytic of experiments show that Mn 0.5 Co 0.5 Fe 2 O 4 is a better photocatalyst compared to the Zn 0.5 Co 0.5 Fe 2 O 4 due to the unique characteristics of Mn 2+ ion which can exist in different oxidation states. Chemistry ; Photocatalysis ; Irradiation ; Visible spectrum ; Photochemistry ; Nuclear chemistry ; Catalysis ; Organic chemistry ; Optoelectronics Pigment Synthesis and Properties ; Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques
Authors: Anna Jędrzejewska | Daniel Sibera | Robert Pełech | Roman Jędrzejewski | Urszula Narkiewicz
Source: Archives of Metallurgy and Materials (article, 2019-03-14)
Field: Materials Science — Materials Chemistry

Nanocomposites graphene/CoFe2O4 and graphene/NiFe2O4 – preparation and characterization A series of nanocomposite graphene/CoFe2O4 and graphene/NiFe2O4 hybrid materials was synthesized via facile, one-pot solvothermal route. The materials were obtained using two pressure methods: synthesis in the autoclave and synthesis in the microwave solvothermal reactor. The use of a microwave reactor enabled to significantly shorten the synthesis time up to 15 min. All the syntheses were carried out in a solution of ethanol. The effect of processing conditions and composite composition on the physicochemical properties and electric conductivity was studied. The specific surface area, density, morphology, phase composition, thermal properties and electric conductivity of the obtained composites were investigated. The results of studies of composites obtained in an autoclave and in a microwave reactor were compared. Graphene ; Materials science ; Nanocomposite ; Characterization (materials science) ; Materials processing ; Nanotechnology ; Industrial chemistry ; Composite material ; Process engineering ; Biochemical engineering Graphene research and applications ; Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials
Authors: М. І. Капіца | Vasiliy Laguta | Yuriy Kozik
Source: MATEC Web of Conferences (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Classification of quality conditions of a traction motor frame insulation of locomotives The objective of the article is to determine the quality conditions of a locomotive traction motor frame insulation based on observations over the recovery voltage and classification of a tested insulation wear degree as well as to determine estimates of operating time until the insulation restoration or replacement with a new one. To classify the insulation quality conditions and to forecast its residual life until the restoration, a cluster analysis and discriminant analysis are used based on indicators reflecting the properties of an insulation recovery voltage curve. The study defines an information content received from frame insulation recovery voltage readings as compared with its resistance from the perspective of testing the insulation condition and predicting its residual life. Statistical characteristics of clusters corresponding to the insulation condition stages allow determining the limit values of predicting parameters and estimating the residual insulation life prior the restoration. To classify the insulation condition, an integrated evaluation of the insulation condition quality is proposed. Integrated evaluation is performed according to the recovery voltage curve and is used as an indicator during clustering. An experimental study of the proposed method practically confirmed its effectiveness. Residual ; Cluster analysis ; Linear discriminant analysis ; Frame (networking) ; Traction (geology) ; Engineering ; Voltage ; Automotive engineering ; Reliability engineering ; Structural engineering ; Computer science ; Mechanical engineering ; Electrical engineering ; Artificial intelligence ; Algorithm Material Properties and Failure Mechanisms ; Railway Engineering and Dynamics ; Transportation Systems and Logistics
Authors: Yuriy Khlibyshyn | Iryna Pochapska | Oleg Grynyshyn | Oleh Hladkyi
Source: Chemical technology and engineering. Proceedings.2019.№1 (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Preparation of bitumen using acidic tars The studies undertaken have established the effect of the ratio of acid tar coupled with straight-run petroleum tars introduced into the process. The influence temperature of the process, of final heating temperature of the bitumen mass in the reactor and of stirring intensity of the reaction mixture in the reactor on the quality is installed. Asphalt ; Chemistry ; Pulp and paper industry ; Environmental chemistry ; Environmental science ; Waste management ; Materials science ; Composite material ; Engineering Petroleum Processing and Analysis
Authors: Muhammed Sami ELGÜN | Nermin Bilgiçli
Source: Akademik Gıda (article, 2019-11-16)
Field: Chemistry — Analytical Chemistry

Buğday Ununa Ozon Gazı Uygulamasının Un ve Ekmek Kalitesine Etkisi Bu araştırmada, farklı buğday çeşitlerine ait unlara uygulanan ozon gazının, un,&amp;nbsp; hamur ve ekmek özellikleri üzerine etkisi araştırılmıştır. Bu amaçla, öğütmeyi takiben iki farklı buğday çeşidine (Bezostaya-1 ve Gerek-79) ait unlara şahide karşılık ozon jeneratörü yardımıyla 0,4 g/h debi ile ozon gazı uygulanmıştır. Un örneklerinde ozonlamanın yapıldığı gün ve 21 günlük dinlendirme sonrasında fiziksel, kimyasal, teknolojik, reolojik ve mikrobiyolojik analizler ile ekmek denemeleri gerçekleştirilmiştir. Ozon uygulaması un örneklerinin sarılık değerini 9.17’den 8.37’e düşürmüştür. 21 gün dinlendirme ve ozon uygulaması sinerjistik etki göstererek unların parlaklık değerini daha fazla artıp, sarılık değerinin daha fazla düşmesine neden olmuştur. Ozon uygulaması buğday unlarının kül, protein, glüten, glüten indeks, düşme sayısı, fenolik madde miktarı ve antioksidan aktivite değerlerinde önemli (p&amp;lt;0.05) bir farklılığa neden olmamıştır. Unlarda düşme sayısı 21 günlük dinlendirme ile 305.25 saniyeden 330.25 saniyeye yükselerek amilolitik aktivitede azalma gerçekleşmiştir. Ozonlama ile unlarda fitik asit miktarı 365.35 mg/100 g’dan 302.48’e düşerken, 21 gün dinlendirme süresi sonunda da unların fitik asit miktarında önemli (p&amp;gt;0.05) azalma gerçekleşmiştir. Genel olarak, 21 günlük dinlendirme ve ozon uygulaması farinograf ve ekstensografta hamur reolojik özellikleri üzerinde olumlu etkide bulunmuştur. Tahmin edildiği gibi Bezostaya-1 buğday unları, Gerek-79 buğday unlarından daha üstün ekmek özellikleri vermiştir. Ozonlama ekmek simetrisi ve gözenek yapısını düzeltmiş, ekmek içi sertliğini önemli düzeyde düşürmüştür. Öğütme sonrası ozon gazı uygulanan unların, 21 gün dinlendirilmiş unlara yakın kalitede un, hamur ve ekmek özellikleri verdiği tespit edilmiştir. Environmental science ; Animal science ; Biology Dye analysis and toxicity ; Hydrogen's biological and therapeutic effects
Authors: Yunchao Xie | Chen Zhang | Xiangquan Hu | Chi Zhang | Steven P. Kelley | Jerry L. Atwood | Jian Lin
Source: ChemRxiv (preprint, 2019-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Synthesis of Metal-Organic Nanocapsules Herein, we report the successful discovery of a new hierarchical structure of metal-organic nanocapsules (MONCs) by integrating chemical intuition and machine learning algorithms. By training datasets from a set of both succeeded and failed experiments, we studied the crystallization propensity of metal-organic nanocapsules (MONCs). Among four machine learning models, XGB model affords the highest prediction accuracy of 91%. The derived chemical feature scores and chemical hypothesis from the XGB model assist to identify proper synthesis parameters showing superior performance to a well-trained chemist. This paper will shed light on the discovery of new crystalline inorganic-organic hybrid materials guided by machine learning algorithms. Nanocapsules ; Intuition ; Computer science ; Artificial intelligence ; Machine learning ; Nanotechnology ; Materials science ; Nanoparticle ; Cognitive science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Zheng Chen
Source: eScholarship (California Digital Library) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning of Big Materials Data In the past decades, theoretical calculations of materials properties have become more accurate and accessible due to the successful development of ab initio codes, as well as advances in computational power. With the booming development of high-throughput computational materials repositories, opportunities have emerged in the area of data-driven discovery of new materials guided by machine learning. However, the interpretation of large materials data sets needs to be performed from an integrated perspective of statistics and materials science intuition. In this thesis, we will address this challenge by demonstrating how the integration of high-throughput software workflows, automated data generation, and machine learning can yield powerful new approaches to materials analysis and optimization. This thesis is broadly divided into two topics. In the first topic (Chapters 2 and 3), we present comprehensive first-principle investigations of the effect of transition metal mixing on layered P2 oxides, using P2 NaxCo0.2Mn0.2Ti0.2Ni0.2Ru0.2O2 as model systems. Our results show that transition metal mixing significantly suppresses the formation of strongly ordered intermediates. Using ab initio molecular dynamics simulations and the climbing image nudged elastic band method, we reveal that transition metal substitution has a pronounced effect on the Na site occupancy energy and Na diffusion energy barriers. By employing a site percolation model, we derive theoretical upper and lower bounds on the concentration of transition metal species in the layered P2 oxides based on their effects on Na diffusion energy barriers. Another key innovation is the use of the MatErials Graph Network (MEGNet) model, a graph-based deep learning approach recently developed in our group, on layered P2 oxides for accurate energy prediction, which we will apply to study mixing energies in a “high-entropy” P2 NaxCo0.2Mn0.2Ti0.2Ni0.2Ru0.2O2. In the second topic (Chapters 4, 5, and 6), we present the development of a first-of-its-kind computational reference XAS database (XASdb). More importantly, we have developed a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm that leverages on ensemble learning techniques to match an unknown target K-edge XANES spectra with computed spectra in XASdb. We will also discuss the development of general machine learning approaches to rapidly and efficiently identify the coordination environment of absorbing atoms from K-edge XANES. Computer science ; Workflow ; Ab initio ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Trevor David Rhone | Robert A. Hoyt | C. R. O'Connor | M. M. Montemore | Challa S. S. R. Kumar | C. M. Friend | Efthimios Kaxiras
Source: arXiv (Cornell University) (preprint, 2019-08-28)
Field: Materials Science — Materials Chemistry

Predicting outcomes of catalytic reactions using machine learning Predicting the outcome of a chemical reaction using efficient computational models can be used to develop high-throughput screening techniques. This can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and expense. Recently, machine learning methods have been bolstering conventional structure-activity relationships used to advance understanding of chemical reactions. We have developed a model to predict the products of catalytic reactions on the surface of oxygen-covered and bare gold using machine learning. Using experimental data, we developed a machine learning model that maps reactants to products, using a chemical space representation. This involves predicting a chemical space value for the products, and then matching this value to a molecular structure chosen from a database. The database was developed by applying a set of possible reaction outcomes using known reaction mechanisms. Our machine learning approach complements chemical intuition in predicting the outcome of several types of chemical reactions. In some cases, machine learning makes correct predictions where chemical intuition fails. We achieve up to 93% prediction accuracy for a small data set of less than two hundred reactions. Chemical space ; Intuition ; Machine learning ; Computer science ; Artificial intelligence ; Chemical reaction ; Outcome (game theory) ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Rainier Barrett | Maghesree Chakraborty | Dilnoza Amirkulova | Heta A. Gandhi | Andrew Dickson White
Source: ChemRxiv (preprint, 2019-04-23)
Field: Materials Science — Materials Chemistry

A GPU-Accelerated Machine Learning Framework for Molecular Simulation: HOOMD-blue with TensorFlow &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;As interest grows in applying machine learning force-fields and methods to molecular simulation, there is a need for state-of-the-art inference methods to use trained models within efficient molecular simulation engines. We have designed and implemented software that enables integration of a scalable GPU-accelerated molecular mechanics engine, HOOMD-blue, with the machine learning (ML) TensorFlow package. TensorFlow is a GPU-accelerated, scalable, graph-based tensor computation model building package that has been the implementation of many recent innovations in deep learning and other ML tasks. TensorFlow models are constructed in Python and can be visualized or debugged using the rich set of tools implemented in the TensorFlow package. In this article, we present four major examples of tasks this software can accomplish which would normally require multiple different tools: (1) we train a neural network to reproduce a force field of a Lennard-Jones simulation; (2) we perform online force matching of methanol; (3) we compute the maximum entropy bias of a Lennard-Jones collective variable; (4) we calculate the scattering profile of an ongoing TIP4P water molecular dynamics simulation. This work should accelerate both the design of new neural network based models in computational chemistry research and reproducible model specification by leveraging a widely-used ML package.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Python (programming language) ; Computer science ; Scalability ; Artificial intelligence ; Machine learning ; Artificial neural network ; Computational science ; Deep learning ; Software ; Computation ; Algorithm ; Programming language Machine Learning in Materials Science ; Parallel Computing and Optimization Techniques ; Protein Structure and Dynamics
Authors: Yuma Iwasaki | Sawada Ryoto | Valentin Stanev | Masahiko Ishida | Akihiro Kirihara | Yasutomo Omori | Hiroko Someya | Ichiro Takeuchi | Eiji Saitoh | Shinichi Yorozu
Source: arXiv (Cornell University) (preprint, 2019-03-06)
Field: Materials Science — Materials Chemistry

Materials development by interpretable machine learning Machine learning technologies are expected to be great tools for scientific discoveries. In particular, materials development (which has brought a lot of innovation by finding new and better functional materials) is one of the most attractive scientific fields. To apply machine learning to actual materials development, collaboration between scientists and machine learning is becoming inevitable. However, such collaboration has been restricted so far due to black box machine learning, in which it is difficult for scientists to interpret the data-driven model from the viewpoint of material science and physics. Here, we show a material development success story that was achieved by good collaboration between scientists and one type of interpretable (explainable) machine learning called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on material science and physics, we interpreted the data-driven model constructed by the FAB/HMEs, so that we discovered surprising correlation and knowledge about thermoelectric material. Guided by this, we carried out actual material synthesis that led to identification of a novel spin-driven thermoelectric material with the largest thermopower to date. Artificial intelligence ; Machine learning ; Identification (biology) ; Inference ; Development (topology) ; Computer science ; Bayesian inference ; Data science ; Bayesian probability ; Mathematics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Expert finding and Q&A systems
Authors: Mahdi Rad | Alexandre Viardin | Göran Schmitz | Markus Apel
Source: arXiv (Cornell University) (preprint, 2019-12-20)
Field: Materials Science — Materials Chemistry

Theory-training deep neural networks for an alloy solidification benchmark problem Deep neural networks are machine learning tools that are transforming fields ranging from speech recognition to computational medicine. In this study, we extend their application to the field of alloy solidification modeling. To that end, and for the first time in the field, theory-trained deep neural networks (TTNs) for solidification are introduced. These networks are trained using the framework founded by Raissi et al.[1-3] and a theory that consists of a mathematical macroscale solidification model and the boundary and initial conditions of a well-known solidification benchmark problem. One of the main advantages of TTNs is that they do not need any prior knowledge of the solution of the governing equations or any external data for training. Using the built-in capabilities in TensorFlow, networks with different widths and depths are trained, and their predictions are examined in detail to verify that they satisfy both the model equations and the initial and boundary conditions of the benchmark problem. Issues that are critical in theory-training are identified, and guidelines that can be used in the future for successful and efficient training of similar networks are proposed. Through this study, theory-trained deep neural networks are shown to be a viable tool to simulate alloy solidification problems. Benchmark (surveying) ; Artificial neural network ; Field (mathematics) ; Computer science ; Deep learning ; Artificial intelligence ; Boundary (topology) ; Training (meteorology) ; Deep neural networks ; Boundary value problem ; Machine learning ; Mathematical optimization ; Algorithm ; Theoretical computer science ; Mathematics ; Geology ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques
Authors: Yan Zhang | Steve Farrell | Michael F. Crowley | Lee Makowski | Jack Deslippe
Source: arXiv (Cornell University) (preprint, 2019-11-15)
Field: Materials Science — Materials Chemistry

A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy. MNIST database ; Benchmark (surveying) ; Microscopy ; Artificial intelligence ; Projection (relational algebra) ; Diffraction ; Computer science ; Deep learning ; Pattern recognition (psychology) ; Machine learning ; Optics ; Physics ; Algorithm ; Geography ; Cartography Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cell Image Analysis Techniques
Authors: May Muslim | Zainab Yousif | Mohamed Abdel Rahman Abdel Ghany
Source: Engineering and Technology Journal (article, 2019-04-25)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Multilayer Graphene Growth by Chemical Vapour Deposition on Co-Ni/Al2O3 Substrate using Artificial Neural Network The uniqueness of multilayer graphene as extremely high carrier mobility, tune-able band gap and high elasticity has made it be considered as a high prospect engineering material that can be employed for several applications such as solar cells, field effect transistors, super-capacitors, batteries and sensors. In this study, the application of Artificial Neural Networks (ANN) for the predictive modeling of multilayer graphene (MLG) growth by chemical vapor deposition (CVD) on Co-Ni/Al2O3 substrate was investigated. Data comprises temperature, catalyst compositions, ethanol flowrates were generated using central composite experimental design and employed to obtain the MLG yield as the response. The data were subsequently used for predictive modeling using ANN. The findings show that the predictive values of the MLG yields were in good agreement with those obtained from the experimental runs having a coefficient of determination (R2 ) of 0.988. Graphene ; Materials science ; Chemical vapor deposition ; Substrate (aquarium) ; Artificial neural network ; Yield (engineering) ; Deposition (geology) ; Chemical engineering ; Nanotechnology ; Computer science ; Composite material ; Machine learning ; Engineering Graphene research and applications ; Radiation Effects in Electronics ; Advancements in Semiconductor Devices and Circuit Design