scholarly journals Structure of an ultrathin oxide film solved by machine learning enhanced global optimization

2021 ◽  
Author(s):  
Lindsay Richard Merte ◽  
Malthe Kjær Bisbo ◽  
Igor Sokolović ◽  
Martin Setvín ◽  
Benjamin Hagman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Global Optimization ◽  
Material Properties ◽  
Experimental Methods ◽  
Solid Surfaces ◽  
Model Generation ◽  
Diverse Population ◽  
Energy Estimation ◽  
Ultrathin Oxide

Determination of the atomic structure of solid surfaces is a challenge that has resisted solution despite advancements in experimental methods. Theory-based global optimization has the potential to revolutionize the field by providing reliable structure models as the basis for interpretation of experiments and for prediction of material properties. So far, however, the approach has been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. We demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4 x 4) surface oxide on Pt3Sn(111)--based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.

Determination of Stiffness Properties of Multi-Ply Cord-Rubber Composites

1989 ◽  
Vol 17 (3) ◽  
pp. 201-216 ◽  
Author(s):  
S. Parhizgar
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Material Properties ◽  
Plate Theory ◽  
Experimental Methods ◽  
Laminated Plate ◽  
Rubber Composites ◽  
Element Analysis ◽  
Stiffness Properties

Abstract The material properties of cord-rubber composites required for finite element analysis of tires are discussed. It is shown that the current experimental methods used in verification of the Laminated Plate Theory have not adequately included the coupling deformations existing in unsymmetrical laminated composites. The importance of these coupling deformations is demonstrated on a 0/90 laminated strip. A special grip system capable of decoupling loads and moments applied to a 0/90 laminated strip is introduced. A procedure for experimental determination of the stiffness constants of 0/90 laminate is given.

Determination of Charge Coefficient of Piezoelectric Films Using a Combined Finite Element Model and Dynamic Loading Technique

2020 ◽  
Vol 835 ◽  
pp. 229-242
Author(s):  
Oboso P. Bernard ◽  
Nagih M. Shaalan ◽  
Mohab Hossam ◽  
Mohsen A. Hassan
Keyword(s):  
Finite Element ◽  
Dynamic Loading ◽  
Material Properties ◽  
Accurate Determination ◽  
Substrate Material ◽  
Experimental Results ◽  
Piezoelectric Composite ◽  
Piezoelectric Film ◽  
Piezoelectric Charge

Accurate determination of piezoelectric properties such as piezoelectric charge coefficients (d33) is an essential step in the design process of sensors and actuators using piezoelectric effect. In this study, a cost-effective and accurate method based on dynamic loading technique was proposed to determine the piezoelectric charge coefficient d33. Finite element analysis (FEA) model was developed in order to estimate d33 and validate the obtained values with experimental results. The experiment was conducted on a piezoelectric disc with a known d33 value. The effect of measuring boundary conditions, substrate material properties and specimen geometry on measured d33 value were conducted. The experimental results reveal that the determined d33 coefficient by this technique is accurate as it falls within the manufactures tolerance specifications of PZT-5A piezoelectric film d33. Further, obtained simulation results on fibre reinforced and particle reinforced piezoelectric composite were found to be similar to those that have been obtained using more advanced techniques. FE-results showed that the measured d33 coefficients depend on measuring boundary condition, piezoelectric film thickness, and substrate material properties. This method was proved to be suitable for determination of d33 coefficient effectively for piezoelectric samples of any arbitrary geometry without compromising on the accuracy of measured d33.

Thickness determination of ultrathin oxide films and its application in magnetic tunnel junctions

2006 ◽  
Vol 35 (12) ◽  
pp. 2142-2146 ◽  
Author(s):  
J. Joshua Yang ◽  
Chengxiang Ji ◽  
Ying Yang ◽  
Y. Austin Chang ◽  
Feng X. Liu ◽  
...  
Keyword(s):  
Tunnel Junctions ◽  
Oxide Films ◽  
Magnetic Tunnel Junctions ◽  
Thickness Determination ◽  
Ultrathin Oxide Films ◽  
Ultrathin Oxide

scholarly journals Ion Beam Nanopatterning of Biomaterial Surfaces

2021 ◽  
Vol 11 (14) ◽  
pp. 6575
Author(s):  
Yu Yang ◽  
Adrian Keller
Keyword(s):  
Material Properties ◽  
Ion Beam ◽  
The Self ◽  
Solid Surfaces ◽  
Beam Irradiation ◽  
Ion Beam Irradiation ◽  
Future Directions ◽  
Self Organized ◽  
Functional Surfaces ◽  
Biomaterial Surfaces

Ion beam irradiation of solid surfaces may result in the self-organized formation of well-defined topographic nanopatterns. Depending on the irradiation conditions and the material properties, isotropic or anisotropic patterns of differently shaped features may be obtained. Most intriguingly, the periodicities of these patterns can be adjusted in the range between less than twenty and several hundred nanometers, which covers the dimensions of many cellular and extracellular features. However, even though ion beam nanopatterning has been studied for several decades and is nowadays widely employed in the fabrication of functional surfaces, it has found its way into the biomaterials field only recently. This review provides a brief overview of the basics of ion beam nanopatterning, emphasizes aspects of particular relevance for biomaterials applications, and summarizes a number of recent studies that investigated the effects of such nanopatterned surfaces on the adsorption of biomolecules and the response of adhering cells. Finally, promising future directions and potential translational challenges are identified.

scholarly journals Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1993
Author(s):  
Fernando Pérez-Sanz ◽  
Miriam Riquelme-Pérez ◽  
Enrique Martínez-Barba ◽  
Jesús de la Peña-Moral ◽  
Alejandro Salazar Nicolás ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Liver Biopsies ◽  
Convenient Technique ◽  
End Stage ◽  
Macrovesicular Steatosis

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

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