jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Y. Uzun | D. Doller | A. E. M. Smink | Minh D. Nguyen | M. P. de Jong | Wilfred G. van der Wiel
Source: Journal of Applied Physics (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Effect of Pb(Zr0.52Ti0.48)O3 thin films on electron transport at the LaAlO3/SrTiO3 interface by surface acoustic waves Surface acoustic waves (SAWs) are capable tools for providing mechanical control over the electronic properties of functional materials. Coupling SAWs with the LaAlO3/SrTiO3 (LAO/STO) conducting interface is particularly interesting as this interface exhibits extraordinary features, such as high mobility at low temperature, ferromagnetism, and superconductivity below 200 mK. For SAW generation, piezoelectricity is indispensable, and due to lack of that in the LAO/STO system, a 200 nm thick Pb(Zr0.52Ti0.48)O3 (PZT) film was grown on top of LAO. SAW excitation and propagation was demonstrated on a PZT/LAO/STO multilayer structure. We further employed SAWs in order to transport free electrons confined to the LAO/STO interface, detected as an acoustoelectric voltage at room temperature. Electrical characterization of the interface was carried out by Van der Pauw measurements. We found that having a PZT layer on top of LAO/STO considerably degraded the interfacial conductivity. The degradation became more pronounced at low temperatures. We attribute these effects to the filling of oxygen vacancies due to interlayer oxygen migration, combined with carrier freeze-out at low temperatures. Materials science ; Van der Pauw method ; Condensed matter physics ; Piezoelectricity ; Electron ; Electrical resistivity and conductivity ; Surface acoustic wave ; Coupling (piping) ; Conductivity ; Optoelectronics ; Hall effect ; Composite material ; Chemistry ; Electrical engineering Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials
Authors: Ilda Abdollahi | Mohammad-Hossein Yas
Source: SN Applied Sciences (article, 2020-03-13)
Field: Materials Science — Materials Chemistry

Free vibration analysis of Timoshenko beams reinforced by BNNTs and a comparison with CNT-reinforced composite Materials science ; Timoshenko beam theory ; Vibration ; Beam (structure) ; Boundary value problem ; Composite number ; Natural frequency ; Composite material ; Boron nitride ; Volume fraction ; Normal mode ; Equations of motion ; Structural engineering ; Classical mechanics ; Mathematics ; Physics ; Mathematical analysis ; Acoustics Nonlocal and gradient elasticity in micro/nano structures ; Tribology and Wear Analysis ; Carbon Nanotubes in Composites
Authors: Ivan Šimeček | А. Н. Залога | Jan Trdlička
Source: Zeitschrift für Kristallographie - Crystalline Materials (article, 2020-06-03)
Field: Materials Science — Materials Chemistry

A new parallel version of a dichotomy based algorithm for indexing powder diffraction data Abstract One of the key parts of the crystal structure solution process from powder diffraction data is the determination of the lattice parameters from experimental data shortly called indexing. The successive dichotomy method is one of the most common ones for this process because it allows an exhaustive search. In this paper, we discuss several improvements for this indexing method that significantly reduces the search space and decrease the solution time. We also propose a combination of this method with other indexing methods: grid search and TREOR. The effectiveness and time-consumption of such algorithm were tested on several datasets, including orthorhombic, monoclinic, and triclinic examples. Finally, we discuss the impacts of the proposed improvements. Triclinic crystal system ; Search engine indexing ; Orthorhombic crystal system ; Monoclinic crystal system ; Computer science ; Algorithm ; Powder diffraction ; Key (lock) ; Lattice (music) ; Grid ; Data mining ; Mathematics ; Crystal structure ; Crystallography ; Information retrieval ; Chemistry ; Geometry ; Physics X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Margaret Bailey | Erin George
Source:  (article, 2020-09-03)
Field: Materials Science — Materials Chemistry

Retrocommissioning (Rcx) Mechanical Systems On A University Campus: Student Capstone Experience Senior engineering students at Rochester Institute of Technology are required to complete a 22week culminating project prior to graduating. This multidisciplinary project assembles teams of students in various engineering majors to work together on an engineering design project sponsored by industry or an academic client. There are a wide range of projects available to students, and all stages of the projects are completed from introductory information given by the sponsor, development of possible design concepts, selection of final concept, analysis and completion of final prototype. In the following paper, the capstone design project process is presented from a student perspective, including a breakdown of the twelve-step process used by the design groups, a course assessment from the student team, as well as details of a specific project as it pertains to the various phases of design. The project involves the development of a retrocommissioning (RCX) test plan for evaluating an existing air handling unit (AHU) on a college campus, in order to reduce energy consumption, improve occupant comfort, and prolong equipment operation. The test plan is implemented and test results are analyzed as part of the student's capstone design experience. In addition, a first and second law thermodynamic analysis is conducted. Based on the team findings, a comprehensive RCX test plan is developed for use on air handling units throughout campus and recommendations are made for retrofit design solutions to improve system performance. Capstone ; Engineering management ; Test (biology) ; Plan (archaeology) ; Engineering education ; Work (physics) ; Process (computing) ; Deliverable ; Engineering ; Computer science ; Systems engineering ; Mechanical engineering Graphite, nuclear technology, radiation studies ; Energy Harvesting in Wireless Networks ; Engineering Education and Curriculum Development
Authors: E. Suhir
Source: Journal of Aerospace Engineering and Mechanics (article, 2020-08-23)
Field: Materials Science — Materials Chemistry

The Outcome of an Engineering Undertaking of Importance Must be Quantified to Assure its Success and Safety: Review The outcome a of crucial engineering undertaking must be quantified at the design/planning stage to assure its success and safety, and since the probability of an operational failure is, in effect, never zero, such a quantification should be done on the probabilistic basis. Some recently published work on the probabilistic predictive modeling (PPM) and probabilistic design for reliability (PDfR) of aerospace electronic and photonic (E&P) products, including human-in-theloop (HITL) problems and challenges, is addressed and briefly reviewed. The effort was lately "brought down to earth" to model possible collision in automated driving (AD). In addition, some problems and tasks beyond the E&P and vehicular engineering field are also addressed with an objective to show how the developed methods and approached can be effectively and fruitfully employed, whenever there is a need to quantify the reliability of an engineering technology with consideration of the human performance. Proton ; Electron ; Irradiation ; Nuclear engineering ; Range (aeronautics) ; Electromagnetic mass ; Radiation ; Materials science ; Nuclear physics ; Atomic physics ; Physics ; Mechanical engineering ; Engineering ; Composite material ; Quantum electrodynamics Graphite, nuclear technology, radiation studies ; Material Properties and Applications ; Radiation Effects and Dosimetry
Authors: Anthony Wang | Steven Kauwe | Ryan Murdock | Taylor D. Sparks
Source: ChemRxiv (preprint, 2020-12-17)
Field: Materials Science — Materials Chemistry

Compositionally-Restricted Attention-Based Network for Materials Property Prediction In this paper, we demonstrate a novel application of the Transformer self-attention mechanism. Our network, the Compositionally-Restricted Attention-Based network, referred to as CrabNet, explores the area of structure-agnostic materials property predictions when only a chemical formula is provided.Our results show that CrabNet's performance matches or exceeds current best practice methods on nearly all of 28 total benchmark datasets. We also demonstrate how CrabNet's architecture lends itself towards model interpretability by showing different visualization approaches that are made possible by CrabNet's design.We feel confident that CrabNet, and its attention-based framework, will be of keen interest to future materials informatics researchers. Interpretability ; Computer science ; Property (philosophy) ; Benchmark (surveying) ; Visualization ; Informatics ; Benchmarking ; Data science ; Transformer ; Data mining ; Artificial intelligence ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Darya Filatova | Charles El-Nouty
Source: International Journal for Computational Civil and Structural Engineering (article, 2020-12-28)
Field: Materials Science — Materials Chemistry

A CRACK DETECTION SYSTEM FOR STRUCTURAL HEALTH MONITORING AIDED BY A CONVOLUTIONAL NEURAL NETWORK AND MAPREDUCE FRAMEWORK The quickly expanded development of artificial intelligence offers alternative ways to solve numerous civil engineering problems. The work is devoted to the development of a computer-vision-based crack detection system capable to process big data related to pathology recognition. In this study, we discuss an automated crack type classification pipeline based on CNN deep learning algorithm and MapReduce framework. The results of numerical modeling illustrate the potential of the crack detection system. Convolutional neural network ; Computer science ; Pipeline (software) ; Process (computing) ; Artificial intelligence ; Big data ; Deep learning ; Machine learning ; Artificial neural network ; Pattern recognition (psychology) ; Data mining Material Properties and Failure Mechanisms ; Industrial Engineering and Technologies ; Engineering Diagnostics and Reliability
Authors: Andrew L. Ferguson | Johannes Hachmann | Thomas F. Miller | Jim Pfaendtner
Source: The Journal of Physical Chemistry C (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

<i>The Journal of Physical Chemistry A</i>/<i>B</i>/<i>C</i> Virtual Special Issue on Machine Learning in Physical Chemistry Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection. Chemistry ; Engineering physics ; Nanotechnology ; Mathematics education ; Engineering ; Psychology ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Aldair E. Gongora | Kelsey L. Snapp | Emily Whiting | Patrick Riley | Kristofer G. Reyes | Elise F. Morgan | Keith A. Brown
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Using Simulation to Accelerate Autonomous Experimentation (AE): A Case Study Using Mechanics Damage mechanics ; Engineering ; Mechanics ; Computer science ; Mechanical engineering ; Structural engineering ; Finite element method ; Physics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Multi-Objective Optimization Algorithms
Authors: Amrita Khan | Dastegir Golam | Mahabub Alam Bhuiyan | Kazi Hanium Maria | Shamima Choudhury | M. Khalid Hossain | Dilip Kumar Saha | Shirin Akhter | S Jadhav | S Shirsath | B Toksha | S Shukla | K Jadhav | P Acharya | R Desai | V Aswal | R Upadhyay | H Mamiya | N Terada | T Furubayashi | H Suzuki | H Kitazawa | D Kim | H Zeng | T Ng | C Brazel | H Yang | C Zhang | X Shi | H Hu | X Du | Y Fang | Y Ma | H Wu | S Yang | S Patange | G Pettit | D Forester | L Antoshina | M Rana | M Ul-Islam | I Ahmed | T Abbas | Q Han | D Ji | G Tang | Z Li | X Hou | W Qi | R Bian | S Liu | N Somaiah | T Jayaraman | P Joy | D Das | A Khan | M Bhuiyan | G Al-Quaderi | K Maria | S Choudhury | M Hossain | S Akther | D Saha | S Bellad | S Watawe | A Shaikh | B Chougule | P Kumar | S Sharma | M Knobel | M Singh | B Karche | B Khasbardar | A Vaingankar | Y Yafet | C Kittel
Source: Biointerface Research in Applied Chemistry (article, 2020-03-29)
Field: Materials Science — Materials Chemistry

Study of the magnetic properties of Zn doped Cobalt ferrite (CoZnxFe2-xO4) Zinc substitution is proposed as an efficient way to improve the performance of the ferrite. In this work, Zinc doped cobalt ferrite with the generic formula CoZnxFe2-xO4, with x = 0.0, 0.1, 0.3 and 0.4 were prepared by conventional ceramic technique, sintered at 1000ο C for 4 hours in air. The effect of zinc concentration on the initial permeability, Curie temperature, saturation magnetization, magnetic moment, coercivity and magnetic susceptibility was investigated. Contrary to most of the work in this subject, here Co is maintained fixed and Fe is progressively varied along with Zn. Curie temperature is found to increase initially up to 459 0C for Zn content of x = 0.1 and then decreased to 1480C for further increase of Zn content (x = 0.4). Effect of Zn on the frequency dependent initial permeability, loss factor and relative quality factor was studied. The maximum value of saturation magnetization (51.37emu/gm) was found for the sample x = 0.1 and then decreases with the increase of Zn content. Morphological and magnetic properties of the samples were studied using scanning electron microscope (SEM) and vibrating sample magnetometer (VSM) respectively. The Zn-doped cobalt-ferrite (x = 0.1) having a high strain derivative could be a potential material for stress sensor application. Coercivity ; Curie temperature ; Materials science ; Scanning electron microscope ; Ferrite (magnet) ; Zinc ; Analytical Chemistry (journal) ; Cobalt ; Doping ; Magnetization ; Magnetic moment ; Magnetometer ; Ceramic ; Nuclear magnetic resonance ; Ferromagnetism ; Metallurgy ; Condensed matter physics ; Composite material ; Chemistry ; Magnetic field Magnetic Properties and Synthesis of Ferrites ; Magnetic Properties and Applications ; Non-Destructive Testing Techniques
Authors: Michał Konarski | P. Węgierek
Source: Metrology and Measurement Systems (article, 2020-03-09)
Field: Materials Science — Materials Chemistry

Pilot implementation of the on-line partial discharge monitoring system for heads of the high voltage cable lines The article presents the assumptions, characteristics and description of the implementation of a pilot systemfor on-line monitoring of partial discharges in heads of the high-voltage cable lines. The main purpose ofthe implementation was to increase the reliability of cable line heads by equipping them with a system ofcontinuous assessment of technical condition with direct transmission of measurement data and alarms tothe SCADA system. In order to achieve the assumed goal, unconventional methods for measuring partialdischarges were used, the application of which does not require disconnecting the line from the voltage.The implementation was carried out on an active 110 kV high voltage cable line in the area of activity ofone of the Distribution System Operators. Partial discharge ; Line (geometry) ; SCADA ; Reliability (semiconductor) ; Voltage ; Engineering ; High voltage ; Electrical engineering ; Transmission line ; Electric power transmission ; Reliability engineering ; Computer science ; Electronic engineering ; Power (physics) ; Physics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Magnetic Field Sensors Techniques
Authors: Muammar El Khatib | Wibe A. de Jong
Source: ChemRxiv (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

ML4Chem: A Machine Learning Package for Chemistry and Materials Science ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and ease of use with demonstrations utilizing neural networks and kernel ridge regression algorithms. Computer science ; Software deployment ; Inference ; Machine learning ; Artificial intelligence ; Visualization ; Kernel (algebra) ; Software engineering ; Data science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Philippe Schwaller | Alain C. Vaucher | Teodoro Laino | Jean‐Louis Reymond
Source: ChemRxiv (preprint, 2020-08-05)
Field: Materials Science — Materials Chemistry

Prediction of Chemical Reaction Yields using Deep Learning &lt;div&gt;Artificial intelligence is driving one of the most important revolutions in organic chemistry. &lt;/div&gt;&lt;div&gt;Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, successfully became part of the organic chemists' daily laboratory, assisting in domain-specific synthetic problems. Unlike reaction prediction and retrosynthetic models, the prediction of reaction yields has received less attention in spite of the enormous potential of accurately predicting reaction conversion rates. Reaction yields models, describing the percentage of the reactants converted to the desired products, could guide chemists and help them select high-yielding reactions and score synthesis routes, reducing the number of attempts. So far, yield predictions have been predominantly performed for high-throughput experiments using a categorical (one-hot) encoding of reactants, concatenated molecular fingerprints, or computed chemical descriptors. Here, we extend the application of natural language processing architectures to predict reaction properties given a text-based representation of the reaction, using an encoder transformer model combined with a regression layer. We demonstrate outstanding prediction performance on two high-throughput experiment reactions sets. An analysis of the yields reported in the open-source USPTO data set shows that their distribution differs depending on the mass scale, limiting the dataset applicability in reaction yields predictions. &lt;/div&gt; Computer science ; Yield (engineering) ; Artificial intelligence ; Categorical variable ; Limiting ; Machine learning ; Chemistry ; Biological system ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Chengqiang Lu | Qi Liu | Qiming Sun | Chang‐Yu Hsieh | Shengyu Zhang | Liang Shi | Chee‐Kong Lee
Source: The Journal of Physical Chemistry C (preprint, 2020-03-11)
Field: Materials Science — Materials Chemistry

Deep Learning for Optoelectronic Properties of Organic Semiconductors Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excited-state electronic-structure calculations, a computationally daunting task for many OSC applications. In this work, we advocate the use of deep learning to address this challenge and demonstrate that state-of-the-art deep neural networks (DNNs) are capable of predicting the electronic properties of OSCs at an accuracy comparable with the quantum chemistry methods used for generating training data. We extensively investigate the performances of four recent DNNs (deep tensor neural network, SchNet, message passing neural network, and multilevel graph convolutional neural network) in predicting various electronic properties of an important class of OSCs, i.e., oligothiophenes (OTs), including their HOMO and LUMO energies, excited-state energies and associated transition dipole moments. We find that SchNet shows the best performance for OTs of different sizes (from bithiophene to sexithiophene), achieving average prediction errors in the range of 20-80meV compared to the results from (time-dependent) density functional theory. We show that SchNet also consistently outperforms shallow feed-forward neural networks, especially in difficult cases with large molecules or limited training data. We further show that SchNet could predict the transition dipole moment accurately, a task previously known to be difficult for feed-forward neural networks, and we ascribe the relatively large errors in transition dipole prediction seen for some OT configurations to the charge-transfer character of their excited states. Finally, we demonstrate the effectiveness of SchNet by modeling the UV-Vis absorption spectra of OTs in dichloromethane and a good agreement is observed between the calculated and experimental spectra. Dipole ; Artificial neural network ; Excited state ; Computer science ; Deep learning ; Convolutional neural network ; Artificial intelligence ; Electronic structure ; Organic semiconductor ; Materials science ; Density functional theory ; Machine learning ; Physics ; Optoelectronics ; Atomic physics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing