jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wentian Jin | Sheriff Sadiqbatcha | Jinwei Zhang | Sheldon X.-D. Tan
Source: arXiv (Cornell University) (preprint, 2020-04-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

EM-GAN: Fast Stress Analysis for Multi-Segment Interconnect Using Generative Adversarial Networks In this paper, we propose a fast transient hydrostatic stress analysis for electromigration (EM) failure assessment for multi-segment interconnects using generative adversarial networks (GANs). Our work leverages the image synthesis feature of GAN-based generative deep neural networks. The stress evaluation of multi-segment interconnects, modeled by partial differential equations, can be viewed as time-varying 2D-images-to-image problem where the input is the multi-segment interconnects topology with current densities and the output is the EM stress distribution in those wire segments at the given aging time. Based on this observation, we train conditional GAN model using the images of many self-generated multi-segment wires and wire current densities and aging time (as conditions) against the COMSOL simulation results. Different hyperparameters of GAN were studied and compared. The proposed algorithm, called {\it EM-GAN}, can quickly give accurate stress distribution of a general multi-segment wire tree for a given aging time, which is important for full-chip fast EM failure assessment. Our experimental results show that the EM-GAN shows 6.6\% averaged error compared to COMSOL simulation results with orders of magnitude speedup. It also delivers 8.3X speedup over state-of-the-art analytic based EM analysis solver. Speedup ; Computer science ; Hyperparameter ; Feature (linguistics) ; Algorithm ; Solver ; Interconnection ; Stress (linguistics) ; Artificial neural network ; Image (mathematics) ; Topology (electrical circuits) ; Parallel computing ; Artificial intelligence ; Mathematics Copper Interconnects and Reliability ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis
Source: IOP Conference Series Materials Science and Engineering (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Preface 1 st International Conference on Computational Engineering and Material Science (ICCEMS-2020), 17-18 July 2020, GM Institute of Technology, Davangere, India Preface This volume of the IOP Conference Series: Materials Science and Engineering contains papers from the 1st International Conference on Computational Engineering and Material Science, which took place virtually from the 17 th to the 18 th of July 2020 at the GM Institute of Technology, Davangere, India. Due to the pandemic of COVID-19 situation, in India there was complete Lockdown announced by the government. In this pandemic gathering was not allowed, not only India, but throughout the world. So, the conference was conducted using Microsoft Teams to connect all the researchers and speakers from the different parts of the world in a single platform and executed successfully the conference without postponing this. The Proceedings presents novel and original work in various engineering domains and its applications by the experts and budding researchers. This conference provided opportunities for researchers to exchange new ideas and application experiences to establish research relations and to find global partners for future collaboration. It covers a variety of topics, including Communication, Concrete, Datamining, Electrical, FEM (Finite Element Method), Image Processing, IoT, Machine Learning, Manufacturing Technology, Material Science, Renewable Energy, Signal Processing, VLSI, WSN. It also provides a platform for researchers, engineers and academicians from all over the world to present their research results and development activities based on these domains. The conference has received paper from We have received papers from Australia, Chile, Philippines. Also from various parts of India: Karnataka, Tamilnadu, Telangana, Punjab, Andhra Pradesh, Uttar Pradesh, Bihar, Assam, Noida, New Delhi. This proceedings volume contains 77 papers presented at the conference. All the papers have been subjected to peer review by at least two referees. Acknowledgements On behalf of the GM Institute of Technology, Davangere, India I would like to thank all the invited speakers, session chairs and members of the discussion panels for making the conference such a great success. Government (linguistics) ; Engineering ; Variety (cybernetics) ; Engineering management ; Library science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Architecture and Computational Design ; Internet of Things and AI
Authors: Raquel de Melo Barbosa | Fabio F. de Oliveira | Gabriel Bezerra Motta Câmara | Tulio Flavio Accioly de Lima e Moura | Fernanda Nervo Raffin | Marcelo A. C. Fernandes
Source:  (article, 2020-12-01)
Field: Materials Science — Biomaterials

Smart Design Nano-Hybrid Formulations by Machine Learning Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms. Solubility ; Support vector machine ; Materials science ; Nano- ; Amphiphile ; Computer science ; Chemical engineering ; Nanotechnology ; Chromatography ; Chemistry ; Composite material ; Artificial intelligence ; Organic chemistry ; Copolymer ; Engineering Clay minerals and soil interactions ; Layered Double Hydroxides Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Felipe Oviedo | Zekun Ren | Hansong Xue | Siyu Tian | Kaicheng Zhang | Mariya Layurova | Thomas Heumueller | Ning Li | Erik Birgersson | Shijing Sun | Benji Mayurama | Ian Marius Peters | Christoph J. Brabec | John W. Fisher | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2020-04-28)
Field: Materials Science — Materials Chemistry

Bridging the gap between photovoltaics R&amp;D and manufacturing with data-driven optimization Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost. So far, solar cell R&amp;D has mostly focused on achieving record efficiencies, a process that often results in small batches, large variance, and limited understanding of the physical causes of underperformance. This approach is intensive in time and resources, and ignores many relevant factors for industrial production, particularly the need for high reproducibility and high manufacturing yield, and the accompanying need of physical insights. The record-efficiency paradigm is effective in early-stage R&amp;D, but becomes unsuitable for industrial translation, requiring a repetition of the optimization procedure in the industrial setting. This mismatch between optimization objectives, combined with the complexity of physical root-cause analysis, contributes to decade-long timelines to transfer new technologies into the market. Based on recent machine learning and technoeconomic advances, our perspective articulates a data-driven optimization framework to bridge R&amp;D and manufacturing optimization approaches. We extend the maximum-efficiency optimization paradigm by considering two additional dimensions: a technoeconomic figure of merit and scalable physical inference. Our framework naturally aligns different stages of technology development with shared optimization objectives, and accelerates the optimization process by providing physical insights. Bridging (networking) ; Process optimization ; Computer science ; Scalability ; Photovoltaics ; Optimization problem ; Yield gap ; Industrial engineering ; Photovoltaic system ; Process engineering ; Production (economics) ; Engineering ; Economics Machine Learning in Materials Science ; Innovation Policy and R&D ; Advanced Semiconductor Detectors and Materials
Authors: F. Baum
Source: Lume (Universidade Federal do Rio Grande do Sul) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Otimização de propriedades físicas de pontos quânticos e nanopartículas de semicondutores por inteligência artificial e planejamento experimental Pontos quânticos são materiais de grande interesse tecnológico, com propriedades ópticas e elétricas aplicáveis em catálise, células solares, biosensores, diodos emissores de luz (LEDs) e displays. A produção em larga escala desse material cresceu nos últimos anos, porém a manufatura de pontos quânticos de alta qualidade e com reprodutibilidade batelada após batelada continua um dos objetivos mais desafiadores para a indústria. Nesse contexto, a inteligência artificial tem se revelado uma excelente ferramenta para entender e controlar sistemas químicos. Na primeira parte deste trabalho, foi construída uma base de dados de sínteses hot injection de pontos quânticos dos tipos II-VI e IV-VI (CdS, CdSe, PbS, PbSe e ZnSe). Essa base de dados foi usada para construir modelos de regressão multilinear, smoothing splines, árvores de regressão, random forest, gradient boosting machine e deep learning. Para a construção de sugestões de novas rotas de síntese, os modelos selecionados foram random forest, gradient boosting machine e deep learning. A partir desses modelos, mapas de calor foram gerados, variando-se temperatura de injeção e tempo de reação, possibilitando a seleção da região com menor variação de diâmetro final. Na segunda parte deste trabalho, o foco foi a busca por alternativas ao uso de semicondutores baseados em cádmio e chumbo. Foram sintetizadas nanopartículas coloidais de sulfeto de cobre e alumínio através do método heat up. As amostras foram analisadas por microscopia eletrônica de transmissão, difratometria de raios-x, espectroscopia Raman, espectroscopia de absorção no UV-Vis-NIR e espectroscopia de fotoluminescência. O efeito dos parâmetros de síntese na intensidade de emissão de fotoluminescência foi avaliado, onde foi possível concluir que o principal parâmetro de síntese é a concentração do precursor de alumínio. As amostras foram aplicadas em microscopia confocal de fluorescência, demonstrando potencial para aplicações como marcadores biológicos. Artificial intelligence ; Computer science Machine Learning in Materials Science ; Chemical and Physical Properties of Materials ; Big Data and Digital Economy
Authors: Cecilia Riccioli | Ana Garrido‐Varo | José Emilio Guerrero-Ginel | Dolores Pérez‐Marín
Source:  (article, 2020-04-23)
Field: Chemistry — Analytical Chemistry

Predicting internal quality parameters of individual pieces of intact oranges using hyperspectral images Conventional methods for the determination of chemical parameters of the fruit like soluble solids and acid content are often complicated and destructive, cannot be run on a large scale and are still far away from being implemented to large volumes of products or even better to individual piece fruits. In this study, the potential of hyperspectral imaging was evaluated for quantifying solid soluble content (SSC) and titratable acidity (TA) in intact oranges. Hyperspectral images (900–1700 nm) of 264 oranges collected during 2017 and 2018 at different maturation stages in Southern Spain farms were recorded. Partial least-squares analysis (PLS), Artificial Neural Network (ANN), optimized Support Vector Machine (SVM) and Gaussian Process Regression (GPR), as well as different spectral pre-processing methods, were tested for their effectiveness in quantifying titratable acidity (TA) and solid soluble content (SSC) in intact oranges. Random samples were chosen to validate the models by cross-validation. The best-selected models were then applied to a validation set of “unknown” samples and standard errors of prediction as well as correlation coefficients between actual and predicted values were calculated. Finally, a prediction map was developed to display the concentration distribution of the TA and SSC in the orange fruit, demonstrating that hyperspectral imaging (HSI) technique was feasible to quantify parameters in citrus fruit and can be further used for monitoring the quality of oranges at pre- and post-harvest in real-time. Hyperspectral imaging ; Quality (philosophy) ; Computer science ; Artificial intelligence ; Computer vision ; Pattern recognition (psychology) ; Remote sensing ; Environmental science ; Geology ; Physics Spectroscopy and Chemometric Analyses
Authors: Susana Marín Aguilar
Source: theses.fr (ABES) (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

Local structure and dynamics of dense colloidal systems : from patchy particles to hard spheres The role played by the structure in determining the dynamics of glassy colloidal systems is still a subject of debate. However, there is compelling evidence of a direct link between changes in the local structure and the dynamical slowdown in glassy systems. Here, we explore the interplay between local structure and dynamics by using patchy particles as glass formers. This is done by making use of molecular dynamics simulations. We show that reinforcing icosahedral geometry causes, the system to exhibit an extreme slowdown in its dynamics. With these results, we provide a route for controlling glassy dynamics through the use of patchy particles. Additionally, an interesting point is whether we can extract information about dynamics from only structural information. In order to explore this point, we simulate a wide variety of hard-sphere mixtures. We show that global dynamics of these systems can be precisely predicted by quantifying the tetrahedrality of the local structure: an order parameter that consists of counting the number of tetrahedra each particle participates in. The predictions of this order parameter maintain their accuracy over a wide variety of densities proving its universality in this family of glass formers. Moreover, it is also capable of capturing the changes in the local dynamics, as regions with high tetrahedrality are strongly correlated with regions with slow dynamics. Finally, we demonstrate that unsupervised machine learning techniques can be used to classify particles with different structural environments, which are strongly correlated to local dynamics. SPHERES ; Dynamics (music) ; Hard spheres ; Colloid ; Statistical physics ; Colloidal particle ; Classical mechanics ; Physics ; Engineering ; Chemical engineering ; Thermodynamics Pickering emulsions and particle stabilization
Authors: A. J. Vincelli | Firas Khatib
Source: Acta Crystallographica Section A Foundations and Advances (article, 2020-08-02)
Field: Materials Science — Materials Chemistry

A consensus method to prepare crystal structures in the Protein Data Bank for downstream applications The vast majority of biomedical and drug discovery projects begin by accessing the Protein Data Bank (PDB). However, the qualities of the protein structures in the PDB (dating back to 1976) vary greatly, which may impact project duration and cost. We used six industry-standard quality metrics to create an aggregate "Quality Score" from 0% to 100%, and then scored all 120,365 protein-containing X-ray structures in the PDB. Though most structures scored well (67% of structures had a Quality Score >90%), we hypothesized that low Quality Scores may be improved to reduce the overall time and cost of projects utilizing these input proteins. We assessed the ability of Rosetta's Relax preparation algorithm to improve the Quality Scores of 500 unique structures, representing a diverse cross-section of the PDB, by Relaxing and validating each structure and then calculating its Quality Score and RMSD after Relaxation. We tested 21 variations of the Relax protocol on this dataset (n = 493), and identified a Pareto optimal Relax variant that increased all Quality Scores (from an average of 91.8 3.6% to 99.4 0.8%) with sub-Angstrom RMSDs from their starting structures (average C RMSD = 0.16 0.61 ). Future work includes incorporating feedback into our consensus method of preparing X-ray structural models, applying this method to all crystal structures in the PDB, and releasing the results and method to the public. Our Pareto optimal Relax protocol may be broadly used to prepare X-ray structures as high-quality inputs for protein engineering projects, and the prepared database may serve as a high-quality dataset for machine learning and other big data applications. Downstream (manufacturing) ; Protein Data Bank ; Materials science ; Protein crystallization ; Downstream processing ; Nanotechnology ; Business ; Chemical engineering ; Chemistry ; Protein structure ; Engineering ; Chromatography ; Biochemistry ; Crystallization Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Mark P. Oxley | Maxim Ziatdinov | Ondrej Dyck | Andrew R. Lupini | Rama K. Vasudevan | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2020-09-22)
Field: Materials Science — Surfaces, Coatings and Films

Probing atomic-scale symmetry breaking by rotationally invariant machine\n learning of multidimensional electron scattering The 4D scanning transmission electron microscopy (STEM) method has enabled\nmapping of the structure and functionality of solids on the atomic scale,\nyielding information-rich data sets containing information on the interatomic\nelectric and magnetic fields, structural and electronic order parameters, and\nother symmetry breaking distortions. A critical bottleneck on the pathway\ntoward harnessing 4D-STEM for materials exploration is the dearth of analytical\ntools that can reduce complex 4D-STEM data sets to physically relevant\ndescriptors. Classical machine learning (ML) methods such as principal\ncomponent analysis and other linear unmixing techniques are limited by the\npresence of multiple point-group symmetric variants, where diffractograms from\neach rotationally equivalent position will form its own component. This\nlimitation even holds for more complex ML methods, such as convolutional neural\nnetworks. Here, we propose and implement an approach for the systematic\nexploration of symmetry breaking phenomena from 4D-STEM data sets using\nrotationally invariant variational autoencoders (rrVAE), which is designed to\ndisentangle the general rotation of the object from other latent\nrepresentations. The implementation of purely rotational rrVAE is discussed as\nare applications to simulated data for graphene and zincblende structures that\nillustrate the effect of site symmetry breaking. Finally, the rrVAE analysis of\n4D-STEM data of vacancies in graphene is illustrated and compared to the\nclassical center-of-mass (COM) analysis. This approach is universal for probing\nof symmetry breaking phenomena in complex systems and can be implemented for a\nbroad range of diffraction methods exploring the 2D diffraction space of the\nsystem, including X-ray ptychography, electron backscatter diffraction (EBSD),\nand more complex methods.\n Symmetry breaking ; Physics ; Invariant (physics) ; Scale invariance ; Artificial intelligence ; Statistical physics ; Theoretical physics ; Computer science ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Geophysical and Geoelectrical Methods
Authors: Sajad Haghanifar
Source: D-Scholarship@Pitt (University of Pittsburgh) (article, 2020-07-31)
Field: Materials Science — Surfaces, Coatings and Films

Multi-functional Optoelectronic Substrates New bio-inspiration, micro-/nanomaterials, and micro-/nanomanufacturing processes offer unprecedented opportunities in engineering optoelectronic substrates for novel photon management strategies, difficult-to-realize material–property combinations, and new multi-functionality. In the past decade, discoveries in the multi-functional properties of micro-/nanostructured surfaces have led to a renaissance of activity in surface engineering, which have transformed substrates for a wide variety of rigid and flexible optoelectronic devices. The most important properties are related to photon management, such as high transparency, antireflection, and haze control. Transparency is the most important property as this determines the amount of light that either goes into or out of the active region of the device. In addition, haze control is an important property for various devices. Displays and touch screens require low optical haze, as high haze can contribute to the blurriness of text and images viewed. In contrast, applications such as solar cells and light emitting diodes (LEDs) would benefit from substrates with both high transparency and high haze. Substrates with high haze can increase how much light scatters into or out of the photoactive layers and may increase the solar cell power conversion efficiency and display or LED extraction efficiency, respectively. In addition to photon management properties, a wide variety of other properties are important that are related to the reliability of the optical properties under a variety of stressors. This includes wettability-related properties such as anti-soiling, self-cleaning, stain-resistance, fog resistance, where it is beneficial for the substrate to maintain its optical properties after exposure to various particulates or liquids. Durability under abrasion, hydrostatic pressure, and repeated bending are also important. Finally, properties such as optical switching may also be useful for various applications. In this study, we summarize our recent research progress in the micro-/nanostructuring of various optoelectronic substrate materials while discussing sources of bio-inspiration, advances in micro-/nanomanufacturing and machine learning strategies we used for fabrication of multi-functional optoelectronic substrates. These engineered surfaces have broad application to a wide variety of substrates for applications such as displays, solar cells, smartphones, light emitting diodes (LEDs), and e-paper, as well as new wearables, RF-ID tags, artificial skin, and medical/health sensors. Materials science ; Haze ; Optoelectronics ; Optical transparency ; Transparency (behavior) ; Nanotechnology ; Substrate (aquarium) ; Transmittance ; Nanomaterials ; Computer science ; Chemistry Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials