1,720,956 research outputs found
Unsupervised Learning of the Structure and Dynamics of Liquid Water
Full text linkThe microscopic description of the local structure of water remains an open challenge. Here, we adopt an agnostic approach to understanding water’s hydrogen bond network using data harvested from molecular dynamics simulations of an empirical water model. A battery of state-of-the-art unsupervised data-science techniques is used to characterize the free energy landscape of water starting from encoding the water environment using local-atomic descriptors, through dimensionality reduction, and finally the use of advanced clustering techniques. Analysis of the free energy at ambient conditions was found to be consistent with a rough single basin and independent of the choice of the water model. We find that the fluctuations of the water network occur in a high-dimensional space which we characterize using a combination of both atomic descriptors and chemical-intuition-based coordinates. We demonstrate that a combination of both types of variables is needed in order to adequately capture the complexity of the fluctuations in the hydrogen bond network at different length scales both at room temperature and also close to the critical point of water. Our results provide a general framework for examining fluctuations in water under different conditions.
We also explore the collective nature of orientational fluctuations on the free energy landscape. Specifically, we develop an unsupervised protocol for identifying reorientational dynamics in liquid water. We show that large swings are more likely
to occur higher up in the free energy landscape than smaller amplitude swings. We show that these orientational fluctuations are collective and occur in waves on the order of tens of picoseconds. These waves of large swings are found to correlate well with the fraction of defects as well as the fluctuations in local densit
High-Dimensional Fluctuations in Liquid Water: Combining Chemical Intuition with Unsupervised Learning
Full text linkThe microscopic description of the local structure of water remainsan open challenge. Here, we adopt an agnostic approach to understanding water'shydrogen bond network using data harvested from molecular dynamics simulationsof an empirical water model. A battery of state-of-the-art unsupervised data-sciencetechniques are used to characterize the free-energy landscape of water starting fromencoding the water environment using local atomic descriptors, throughdimensionality reduction andfinally the use of advanced clustering techniques.Analysis of the free energy under ambient conditions was found to be consistentwith a rough single basin and independent of the choice of the water model. Wefind that thefluctuations of the water network occur in a high-dimensional space,which we characterize using a combination of both atomic descriptors andchemical-intuition-based coordinates. We demonstrate that a combination of bothtypes of variables is needed in order to adequately capture the complexity of thefluctuations in the hydrogen bond network atdifferent length scales both at room temperature and also close to the critical point of water. Our results provide a general frameworkfor examiningfluctuations in water under different conditions
Beyond local structures in critical supercooled water through unsupervised learning
Full text linkThe presence of a second critical point in water has been a topic of intense investigation for the last few decades. The molecular origins underlying this phenomenon are typically rationalized in terms of the competition between local high-density (HD) and low-density (LD) structures. Their identification often requires designing parameters that are subject to human intervention. Herein, we use unsupervised learning to discover structures in atomistic simulations of water close to the liquid-liquid critical point (LLCP). Encoding the information on the environment using local descriptors, we do not find evidence for two distinct thermodynamic structures. In contrast, when we deploy nonlocal descriptors that probe instead heterogeneities on the nanometer length scale, this leads to the emergence of LD and HD domains rationalizing the microscopic origins of the density fluctuations close to criticality
The Collective Burst Mechanism of Angular Jumps in Liquid Water
Full text linkUnderstanding the microscopic origins of collective reorientational motions
in aqueous systems requires techniques that allow us to reach beyond our
chemical imagination. Herein, we elucidate a mechanism using unsupervised
learning, showing that large angular jumps in liquid water involve highly
cooperative orchestrated motions. Our automatized detection of angular
fluctuations, unravels a heterogeneity in the type of angular jumps occurring
concertedly in the system. We show that large orientational motions require a
highly collective dynamic process involving correlated motion of up to 10% of
water molecules in the hydrogen-bond network that form spatially connected
clusters. This phenomenon is rooted in the collective fluctuations of the
network topology which results in the creation of defects in waves on the ThZ
timescale. The mechanism we propose involves a cascade of hydrogen-bond
fluctuations underlying angular jumps and provides new insights into the
current localized picture of angular jumps, and in its wide use in the
interpretations of numerous spectroscopies as well in reorientational dynamics
of water near biological and inorganic systems.Comment: 12 pages, 7 figures, Supplementary Information as ancillary fil
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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