jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehmet F. Demirel | Shengchao Liu | Siddhant Garg | Zhenmei Shi | Yingyu Liang
Source: arXiv (Cornell University) (preprint, 2021-10-06)
Field: Materials Science — Materials Chemistry

Attentive Walk-Aggregating Graph Neural Networks Graph neural networks (GNNs) have been shown to possess strong representation power, which can be exploited for downstream prediction tasks on graph-structured data, such as molecules and social networks. They typically learn representations by aggregating information from the $K$-hop neighborhood of individual vertices or from the enumerated walks in the graph. Prior studies have demonstrated the effectiveness of incorporating weighting schemes into GNNs; however, this has been primarily limited to $K$-hop neighborhood GNNs so far. In this paper, we aim to design an algorithm incorporating weighting schemes into walk-aggregating GNNs and analyze their effect. We propose a novel GNN model, called AWARE, that aggregates information about the walks in the graph using attention schemes. This leads to an end-to-end supervised learning method for graph-level prediction tasks in the standard setting where the input is the adjacency and vertex information of a graph, and the output is a predicted label for the graph. We then perform theoretical, empirical, and interpretability analyses of AWARE. Our theoretical analysis in a simplified setting identifies successful conditions for provable guarantees, demonstrating how the graph information is encoded in the representation, and how the weighting schemes in AWARE affect the representation and learning performance. Our experiments demonstrate the strong performance of AWARE in graph-level prediction tasks in the standard setting in the domains of molecular property prediction and social networks. Lastly, our interpretation study illustrates that AWARE can successfully capture the important substructures of the input graph. The code is available on $\href{https://github.com/mehmetfdemirel/aware}{GitHub}$. Interpretability ; Computer science ; Weighting ; Theoretical computer science ; Graph ; Expressive power ; Artificial intelligence ; Graph property ; Adjacency list ; Machine learning ; Data mining ; Algorithm ; Line graph ; Voltage graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: S. S. Burdin
Source: Экономика и предпринимательство (article, 2021-02-16)
Field: Materials Science — General Materials Science

Conceptual model of life cycle management for science-intensive products of the aviation industry Исследовано понятие «жизненный цикл наукоёмкой продукции», проведен анализ концепций жизненного цикла, исследовано современное состояние российской авиационной промышленности (АП), выделены проблемы, препятствующие выполнению программы развития АП. Разработана концептуальная модель управления жизненным циклом наукоёмкой продукции. Выделены информационные, аналитические, функциональные и управленческие слои. Основным аналитическим слоем которой является оценка стратегического риска. Предлагается портфельные компоненты стратегического риска наукоёмкого изделия оценивать с использованием обучающейся нейронной сети, которая анализирует степень соответствия компонент стратегического риска, мировым тенденциям, требованиям заказчиков, развитию материалов, технологий. The concept of "life cycle of high technology products" is investigated, the analysis of life cycle concepts is carried out, the current state of the Russian aviation industry (AI) is investigated, the problems that hinder the implementation of the AI development program are highlighted. A conceptual model of life cycle management for high technology products has been developed. The informational, analytical, functional and management layers are highlighted. The main analytical layer of which is the assessment of strategic risk. It is proposed to evaluate the portfolio components of the strategic risk of a science-intensive product using a learning neural network that analyzes the degree of compliance of the strategic risk components with global trends, customer requirements, development of materials and technologies. Aviation ; Conceptual model ; Aeronautics ; Engineering ; Business ; Computer science ; Aerospace engineering Material Properties and Applications ; Technology Assessment and Management
Authors: Alexander van Roessel | U. von Toussaint | A. Manhard
Source: Nuclear Materials and Energy (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Convolutional neural networks for the in-situ investigation of blistering on plasma-exposed metal surfaces The exposure of metal surfaces to energetic particles originating for example from a plasma can lead to the formation of blisters. These features are caused by hydrogen-filled gas bubbles near the surface and can influence the diffusion and retention of hydrogen atoms in the bulk material during plasma exposure. No comprehensive theory of the underlying processes governing these effects has been developed so far. A major constraint to the investigation of blisters has been the effort that is required to identify and characterize a statistically relevant ensemble of blisters. Therefore, the analysis of a limited number of blisters in isolated observations before and after plasma exposure has been state of the art so far.In this article, a framework is presented that allows to automatically analyze blisters in image data with the aid of convolutional neural networks (CNNs) that are trained with artificial training data. It thereby resolves the limitations of existing analysis methods that prevented the large-scale and in-situ investigation of blistering up to now. Automated routines are used to identify and localize blisters, estimate important blister parameters such as size, and track individual blisters over time. Apart from discussing the principles underlying this approach and describing the implementation of the framework in detail, this article also presents first results acquired with a molybdenum sample that was exposed to a low-temperature deuterium plasma. The results indicate a blister identification accuracy of the framework of more than 80% within the considered parameter ranges and overall demonstrate the feasibility of the approach in general. Convolutional neural network ; In situ ; Plasma ; Metal ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Metallurgy ; Physics ; Nuclear physics ; Organic chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; Laser-induced spectroscopy and plasma
Authors: Ting Yu Teo | Li Lu | Robert E. Simpson
Source: arXiv (Cornell University) (preprint, 2021-06-02)
Field: Materials Science — Materials Chemistry

Reconfigurable multi-bit phase change material silicon photonics directional couplers The unique optical properties of phase change materials (PCMs) can be exploited to develop efficient reconfigurable photonic devices. Here, we design, model, and compare the performance of programmable 1X2 optical couplers based on: Ge$_2$Sb$_2$Te$_5$, Ge$_2$Sb$_2$Se$_4$Te$_1$, Sb$_2$Se$_3$, and Sb$_2$S$_3$ PCMs. Once programmed, these devices are passive, which can reduce the overall power used in an ONN. In the telecommunication wavelength region (1520 to 1580 nm), the Sb$_2$S$_3$ directional coupler displays a minimum insertion loss of -0.04 dB and -1.1 dB, and the lowest crosstalk of -42 dB and -17 dB for the amorphous and crystalline states respectively. We also design a non-volatile 2-bit low-loss tuneable Sb$_2$S$_3$ directional coupler that can be used to set the weights of optical neural networks (ONN). Partial crystallization of Sb$_2$S$_3$ strips with dimensions compatible to those in the directional coupler indicate that at least four different crystalline lengths can be reliably programmed. We used this effect to design a 2-bit tuneable Sb$_2$S$_3$ directional coupler with a weight dynamic range close to 17 dB. The bit-depth of the coupler appears to be limited by the crystallization stochasticity. Power dividers and directional couplers ; Materials science ; Photonics ; Optoelectronics ; Optics ; Amorphous solid ; Wavelength ; Optical switch ; Phase (matter) ; Physics ; Chemistry Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Advanced Memory and Neural Computing
Authors: P. Prashanth | Elammaran Jayamani | Kok Heng Soon | Yat Choy Wong
Source: Materialwissenschaft und Werkstofftechnik (article, 2021-10-01)
Field: Materials Science — Polymers and Plastics

Determination of dielectric properties of natural fiber reinforced polymer composite using adaptive neuro fuzzy inference system Abstract In this study, the dielectric properties of polylactic acid reinforced with natural fibers are objectified by using adaptive neuro fuzzy inference system (ANFIS). This method is considered to be economically feasible as compared to fabricating dielectric samples and measuring dielectric properties with apt operating conditions and advanced equipment. Also, past research has focused primarily on polymer dielectric properties which does not involve any natural fiber inclusion, such that this research will focus on producing ANFIS models for natural fibers which will then be used to calculate dielectric permittivity ( ), dielectric loss ( ) with respect to frequency dependencies. Furthermore, experimental results of dielectric constants and losses of polylactic acid (PLA) based composites will be analyzed from past research which used physical techniques to fabricate composites and will be compared with the results from ANFIS models. It was found that error computation of both the properties are found to be low and the percentage difference in output data is considered less. In addition, the ANFIS neural network had predicted most of the data that was trained, and the crisp output values were found to correlate with the experimental dielectric properties which makes this prediction model possible to use as an alternative approach to fabricating composites and testing. Dielectric ; Adaptive neuro fuzzy inference system ; Composite material ; Materials science ; Permittivity ; Polylactic acid ; Fiber ; Dielectric loss ; Composite number ; Artificial neural network ; Polymer ; Computer science ; Fuzzy logic ; Fuzzy control system ; Machine learning ; Artificial intelligence ; Optoelectronics Natural Fiber Reinforced Composites ; Dielectric materials and actuators ; Smart Materials for Construction
Authors: Sayantan Das | Joey Hung | Sandip Halder | Roy Koret | Igor Turovets | Anne-Laure Charley | Philippe Leray
Source: Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV (article, 2021-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Scatterometry solutions for 14nm half-pitch BEOL layers patterned by EUV single exposure To keep up with logic area scaling, BEOL dimensions have been reduced at an accelerated pace, leading to ever smaller metal pitches and reduced cross-sectional areas of the wires. As a result, routing congestion and a dramatic RC delay (resulting from an increased resistance-capacitance (RC) product) have become important bottlenecks for further interconnect scaling, driving the need for introducing new materials and integration schemes in the BEOL. The current paper studies the damascene process flow that uses a single exposure EUV to create metal lines and 2D patterns at metal half-pitch of 14nm, corresponding to the imec N5 node for logic BEOL layer. A bright field mask with a negative tone resist process was used to develop trenches and transfer these patterns into an oxide dielectric layer. Following this, the trenches were filled with ruthenium (Ru) for electrically testing. Test vehicle included multiple structures, including E-test resistance and capacitance structures, to allow a comprehensive study of the proposed process flow. Metrology requirements and performance at various process steps will be discussed in this paper. Our focus will be on the scatterometry methods that together with machine learning (ML) allow fast and accurate measurements of multiple parameters of interest at large sampling. In the current paper, we present results for inline measurements of line and space critical dimensions (CD), line edge roughness (LER) – after patterning and after hard mask etch, and the prediction of the electrical performance of the metal lines after Ru CMP. In addition, scatterometry ML capabilities for inline tip-to- tip (T2T) measurements are successfully demonstrated. Back end of line ; Materials science ; Extreme ultraviolet lithography ; Critical dimension ; Reticle ; Resist ; Lithography ; Capacitance ; Multiple patterning ; Copper interconnect ; Optoelectronics ; Interconnection ; Electronic engineering ; Node (physics) ; Wafer ; Computer science ; Dielectric ; Nanotechnology ; Layer (electronics) ; Optics ; Acoustics ; Engineering ; Telecommunications ; Physics Copper Interconnects and Reliability ; Advancements in Photolithography Techniques ; Optical Coatings and Gratings
Authors: Leonardo Medrano Sandonas | Johannes Hoja | Brian G. Ernst | Álvaro Vázquez‐Mayagoitia | Robert A. DiStasio | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2021-10-18)
Field: Materials Science — Materials Chemistry

Quantum Mechanics Enables "Freedom of Design" in Molecular Property Space Rational design of molecules with targeted properties requires understanding quantum-mechanical (QM) structure-property/property-property relationships (SPR/PPR) across chemical compound space. We analyze these relationships using the QM7-X dataset---which includes multiple QM properties for ~4.2 M equilibrium and non-equilibrium structures of small (primarily organic) molecules. Instead of providing simple SPR/PPR that strictly follow physicochemical intuition, our analysis uncovers substantial flexibility in molecular property space (MPS) when searching for a single molecule with a desired pair of QM properties or distinct molecules with a targeted set of QM properties. As proof-of-concept, we used Pareto multi-property optimization to search for the most promising (i.e., highly polarizable and electrically stable) molecules for polymeric battery materials; without prior knowledge of this complex manifold of MPS, Pareto front analysis reflected this intrinsic flexibility and identified small directed structural/compositional changes that simultaneously optimize these properties. Our analysis of such extensive QM property data provides compelling evidence for an intrinsic “freedom of design” in MPS, and indicates that rational design of molecules with a diverse array of targeted QM properties is quite feasible. Chemical space ; Intuition ; Molecule ; Property (philosophy) ; Flexibility (engineering) ; Rational design ; Computer science ; Quantum chemical ; Configuration space ; Polarizability ; Topology (electrical circuits) ; Quantum ; Computational chemistry ; Nanotechnology ; Materials science ; Statistical physics ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Semyon V. Bachinin | Venera Gilemkhanova | Maria Timofeeva | Yuliya Kenzhebayeva | Andrei N. Yankin | Valentin A. Milichko
Source: Chimica Techno Acta (article, 2021-08-27)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks for Metal-Ion Batteries: Towards Scalability Metal-organic frameworks (MOFs), being a family of highly crystalline and porous materials, have attracted particular attention in material science due to their unprecedented chemical and structural tunability. Next to their application in gas adsorption, separation, and storage, MOFs also can be utilized for energy transfer and storage in batteries and supercapacitors. Based on recent studies, this review describes the latest developments about MOFs as structural elements of metal-ion battery with a focus on their industry-oriented and large-scale production. Metal-organic framework ; Supercapacitor ; Materials science ; Energy storage ; Nanotechnology ; Battery (electricity) ; Porosity ; Adsorption ; Metal ; Electrode ; Chemistry ; Metallurgy ; Composite material ; Capacitance ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Supercapacitor Materials and Fabrication
Authors: Yunzhe Wang | Shanping Liu | Peter Lile | Sam Walton Norwood | Alberto Hernandez | Sukriti Manna | Tim Mueller
Source: ChemRxiv (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

Accelerated Prediction of Atomically Precise Cluster Structures Using On-the-fly Machine Learning The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the stable structures of clusters can be computationally expensive. In this work, we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned on-the-fly. Applying this approach to aluminum clusters with 21 to 55 atoms, we have identified structures with lower energy than any reported in the literature for 25 out of the 35 sizes. Our benchmarks indicate that the active learning procedure accelerated the average search speed by more than an order of magnitude relative to genetic algorithm searches using only density functional calculations. This work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters. Nanoclusters ; Cluster (spacecraft) ; Transferability ; Computer science ; Work (physics) ; On the fly ; Energy (signal processing) ; Genetic algorithm ; Algorithm ; Machine learning ; Materials science ; Physics ; Nanotechnology Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Boron and Carbon Nanomaterials Research
Authors: Musa Kazım Acar | Türkan Altun | İlkay Hilal Gübbük
Source: Research Square (preprint, 2021-03-22)
Field: Chemistry — Organic Chemistry

Synthesis and characterization of silver doped magnetic-nanoclay montmorillonite/iron oxide/silver nanocomposite for environmental applications throught effective dye degradation Abstract Magnetic-nanoclay montmorillonite/iron oxide (MMT/Fe 3 O 4 ) nanocomposite was prepared by co-precipitation method and montmorillonite/iron oxide/silver (MMT/Fe 3 O 4 /Ag) nanocomposite was synthesized, called as chemical reduction method, by reduction of silver nitrate (AgNO 3 ) salt with sodium borohydride (NaBH 4 ) as reducing agent. These nanocomposites were characterized by Branuer-Emmet-Teller Surface Area and Porosity Dimension (BET), X-Ray Diffractometer (XRD), Fourier Transform Infrared Spectrum (FTIR), Scanning Electron Microscope (SEM) and Vibrating Sample Magnetometer (VSM). Subsequently, MMT/Fe 3 O 4 /Ag was used in degradation of Rhodamine B (RhB) in the presence of NaBH 4 to evaluate catalytic activity of its and consequences were monitored using UV-Visible Spektrofotometer (UV-Vis). Moreover, various parameters including pH, catalyst dosage, initial dye concentration and amount of NaBH 4 were investigated for optimum reaction conditions. The reduction reactions followed pseudo-first order kinetics and complete degradation of RhB were achieved in 8 minutes using MMT/Fe 3 O 4 /Ag as catalysts while degradation of %46 of RhB were achieved in 60 minutes using MMT/Fe 3 O 4 . Nanocomposite ; Montmorillonite ; Degradation (telecommunications) ; Characterization (materials science) ; Doping ; Materials science ; Iron oxide ; Chemical engineering ; Oxide ; Nanotechnology ; Composite material ; Metallurgy ; Optoelectronics ; Computer science ; Engineering Nanomaterials for catalytic reactions ; Nanoparticle-Based Drug Delivery ; Adsorption and biosorption for pollutant removal
Authors: Qiyuan Zhao | Yinan Xu | Jeffrey Greeley | Brett M. Savoie
Source: ChemRxiv (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

Deep Reaction Network Exploration at a Heterogeneous Catalytic Interface Characterizing the reaction energies and reaction barriers of complex reaction networks is central to catalyst development and optimization. Nevertheless, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large system sizes and open-ended reactant lists, that make ad hoc network construction and characterization the current state-of-the-art. Here we show how automated algorithms for exploring and characterizing reaction networks can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single site Ga3+ catalysts as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and system size for identifying network terminations, a comprehensive reaction network was generated for this system and validated against standard methods. The automated algorithm (re)discovers the classic Cossee-Arlman mechanism for this system that is hypothesized to drive major product formation while remarkably also predicting several new pathways for producing alkanes and coke precursors. This demonstration represents the largest heterogeneous catalyst (more than 50 atoms, with an open-ended pool of reactants) to be characterized using a quantum chemistry-based automated reaction method. Catalysis ; Chemical reaction engineering ; Computer science ; Heterogeneous catalysis ; Reaction mechanism ; Network formation ; Network model ; Characterization (materials science) ; Nanotechnology ; Chemistry ; Distributed computing ; Materials science ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Javier Vela | Emily A. Weiss
Source: Inorganic Chemistry (article, 2021-04-26)
Field: Chemistry — Physical and Theoretical Chemistry

Periodic TableTalks: The Elements Never Go Out of Style With the cancelation of the Spring ACS National meeting in 2020—and subsequent in-person meetings—because of the COVID-19 pandemic, the Division of Inorganic Chemistry (DIC) sought new ways to connect with its membership and continue to showcase the fantastic research carried out by inorganic chemists. The DIC executive committee thus initiated monthly Zoom webinars with two speakers each, typically one junior investigator and one more-established researcher, selected with input from each of the DIC subdivisions: Bioinorganic, Coordination, Solid-State, Organometallic, Nanoscience, and Sustainable Energy &amp; Environment. Chemistry ; Style (visual arts) ; Archaeology ; History History and advancements in chemistry ; Chemistry and Stereochemistry Studies