7 research outputs found
Advanced Statistical Methods for Atomic-Level Quantification of Multi-Component Alloys
This thesis comprises a collection of papers whose common theme is data analysis of high entropy alloys. The experimental technique used to view these alloys at the nano-scale produces a dataset that, while comprised of approximately 10^7 atoms, is corrupted by observational noise and sparsity. Our goal is to developstatistical methods to quantify the atomic structure of these materials. Understanding the atomic structure of these materials involves three parts: 1. Determining the crystal structure of the material 2. Finding the optimal transformation onto a reference structure 3. Finding the optimal matching between structures and the lattice constantFrom identifying these elements, we may map a noisy and sparse representation of an HEA onto its reference structure and determine the probabilities of different elemental types that are immediately adjacent, i.e., first neighbors, or are one-level removed and are second neighbors. Having these elemental descriptors of a material, researchers may then develop interaction potentials for molecular dynamics simulations, and make accurate predictions about these novel metallic alloys
Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling
The role of epidemiological models is crucial for informing public health
officials during a public health emergency, such as the COVID-19 pandemic.
However, traditional epidemiological models fail to capture the time-varying
effects of mitigation strategies and do not account for under-reporting of
active cases, thus introducing bias in the estimation of model parameters. To
infer more accurate parameter estimates and to reduce the uncertainty of these
estimates, we extend the SIR and SEIR epidemiological models with two
time-varying parameters that capture the transmission rate and the rate at
which active cases are reported to health officials. Using two real data sets
of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models
with time-varying transmission and reporting rates and via their standard
counterparts with constant rates; our approach provides parameter estimates
with more realistic interpretation, and one-week ahead predictions with reduced
uncertainty. Furthermore, we find consistent under-reporting in the number of
active cases in the data that we consider, suggesting that the initial phase of
the pandemic was more widespread than previously reported
Materials Fingerprinting Classification
Significant progress in many classes of materials could be made with the
availability of experimentally-derived large datasets composed of atomic
identities and three-dimensional coordinates. Methods for visualizing the local
atomic structure, such as atom probe tomography (APT), which routinely generate
datasets comprised of millions of atoms, are an important step in realizing
this goal. However, state-of-the-art APT instruments generate noisy and sparse
datasets that provide information about elemental type, but obscure atomic
structures, thus limiting their subsequent value for materials discovery. The
application of a materials fingerprinting process, a machine learning algorithm
coupled with topological data analysis, provides an avenue by which
here-to-fore unprecedented structural information can be extracted from an APT
dataset. As a proof of concept, the material fingerprint is applied to
high-entropy alloy APT datasets containing body-centered cubic (BCC) and
face-centered cubic (FCC) crystal structures. A local atomic configuration
centered on an arbitrary atom is assigned a topological descriptor, with which
it can be characterized as a BCC or FCC lattice with near perfect accuracy,
despite the inherent noise in the dataset. This successful identification of a
fingerprint is a crucial first step in the development of algorithms which can
extract more nuanced information, such as chemical ordering, from existing
datasets of complex materials
Materials Fingerprinting Classification
Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. This successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials