3,861 research outputs found
Identification of structure in condensed matter with the topological cluster classification
We describe the topological cluster classification (TCC) algorithm. The TCC
detects local structures with bond topologies similar to isolated clusters
which minimise the potential energy for a number of monatomic and binary simple
liquids with particles. We detail a modified Voronoi bond detection
method that optimizes the cluster detection. The method to identify each
cluster is outlined, and a test example of Lennard-Jones liquid and crystal
phases is considered and critically examined.Comment: 28 pages, 28 figure
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
When metallic glasses (MGs) are subjected to mechanical loads, the plastic
response of atoms is non-uniform. However, the extent and manner in which
atomic environment signatures present in the undeformed structure determine
this plastic heterogeneity remain elusive. Here, we demonstrate that novel site
environment features that characterize interstice distributions around atoms
combined with machine learning (ML) can reliably identify plastic sites in
several Cu-Zr compositions. Using only quenched structural information as
input, the ML-based plastic probability estimates ("quench-in softness" metric)
can identify plastic sites that could activate at high strains, losing
predictive power only upon the formation of shear bands. Moreover, we reveal
that a quench-in softness model trained on a single composition and quenching
rate substantially improves upon previous models in generalizing to different
compositions and completely different MG systems (Ni62Nb38, Al90Sm10 and
Fe80P20). Our work presents a general, data-centric framework that could
potentially be used to address the structural origin of any site-specific
property in MGs
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