Unsupervised Machine Learning
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2021 ◽  
Vol 249 ◽  
pp. 113250
Emmanuel Akintunde ◽  
Saeed Eftekhar Azam ◽  
Ahmed Rageh ◽  
Daniel G. Linzell

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2706
Haotian Wen ◽  
José María Luna-Romera ◽  
José C. Riquelme ◽  
Christian Dwyer ◽  
Shery L. Y. Chang

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

2021 ◽  
Sergei V Kalinin ◽  
James Steffes ◽  
Yongtao Liu ◽  
Bryan Huey ◽  
Maxim Ziatdinov

2021 ◽  
Xianda Gong ◽  
Heike Wex ◽  
Thomas Müller ◽  
Silvia Henning ◽  
Jens Voigtländer ◽  

Abstract. The Cape Verde Atmospheric Observatory (CVAO), which is influenced by both, marine and desert dust air masses, has been used for long-term measurements of different properties of the atmospheric aerosol from 2008 to 2017. These properties include particle number size distributions (PNSD), light absorbing carbon (LAC) and concentrations of cloud condensation nuclei (CCN) together with their hygroscopicity. Here we summarize the results obtained for these properties and use an unsupervised machine learning algorithm for the classification of aerosol types. Five types of aerosols, i.e., marine, freshly-formed, mixture, moderate dust and heavy dust, were classified. Air masses during marine periods are from the Atlantic Ocean and during dust periods are from the Sahara. Heavy dust was more frequently present during wintertime, whereas the clean marine periods were more frequently present during springtime. It was observed that during the dust periods CCN number concentrations at a supersaturation of 0.30 % are roughly 2.5 times higher than during marine periods, but the hygroscopicity (κ) of particles in the size range from ∼30 to ∼175 nm during marine and dust periods are comparable. The long-term data presented here, together with the aerosol classification, can be used as a base to improve our understanding of annual cycles of the atmospheric aerosol in the eastern tropical Atlantic and on aerosol-cloud interactions and it can be used as a base for driving, evaluating and constraining atmospheric model simulations.

2021 ◽  
Petar Zhivkov

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6099
James Ming Chen ◽  
Mobeen Ur Rehman

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy.

2021 ◽  
pp. 1-11
Ning Wang ◽  
Christoph Freysoldt ◽  
Siyuan Zhang ◽  
Christian H. Liebscher ◽  
Jörg Neugebauer

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.

2021 ◽  
Vol 12 (1) ◽  
Andrij Vasylenko ◽  
Jacinthe Gamon ◽  
Benjamin B. Duff ◽  
Vladimir V. Gusev ◽  
Luke M. Daniels ◽  

AbstractThe selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li3.3SnS3.3Cl0.7. The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing.

Fernando A. Assis ◽  
Alex J. C. Coelho ◽  
Lucas D. Rezende ◽  
Armando M. Leite da Silva ◽  
Leonidas C. Resende

Solar Energy ◽  
2021 ◽  
Vol 227 ◽  
pp. 447-456
Dipti Ranjan Parida ◽  
Nikhil Dani ◽  
Saptarshi Basu

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