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Data Science in Scanning Probe Micro...

Dusch, William.

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Data Science in Scanning Probe Microscopy: Advanced Analytics and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data Science in Scanning Probe Microscopy: Advanced Analytics and Machine Learning./
Author:	Dusch, William.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	158 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Computational chemistry. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13917899
ISBN:	9781392318324

Data Science in Scanning Probe Microscopy: Advanced Analytics and Machine Learning.
Dusch, William.

Data Science in Scanning Probe Microscopy: Advanced Analytics and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 158 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2019.

This item must not be added to any third party search indexes.

Scanning probe microscopy (SPM) has allowed researchers to measure materials' structural and functional properties, such as atomic displacements and electronic properties at the nanoscale. Over the past decade, great leaps in the ability to acquire large, high resolution datasets have opened up the possibility of even deeper insights into materials. Unfortunately, these large datasets pose a problem for traditional analysis techniques (and software), necessitating the development of new techniques in order to better understand this new wealth of data.Fortunately, these developments are paralleled by the general rise of big data and the development of machine learning techniques that can help us discover and automate the process of extracting useful information from this data. My thesis research has focused on bringing these techniques to all aspects of SPM usage, from data collection through analysis. In this dissertation I present results from three of these efforts: the improvement of a vibration cancellation system developed in our group via the introduction of machine learning, the classification of SPM images using machine vision, and the creation of a new data analysis software package tailored for large, multidimensional datasets which is highly customizable and eases performance of complex analyses.Each of these results stand on their own in terms of scientific impact - for example, the machine learning approach discussed here enables a roughly factor of two to three improvement over our already uniquely successful vibration cancellation system. However, together they represent something more - a push to bring machine learning techniques into the field of SPM research, where previously only a handful of research groups have reported any attempts, and where all efforts to date have focused on analysis, rather than collection, of data. These results also represent first steps in the development of a "driverless SPM" where the SPM could, on its own, identify, collect, and begin analysis of scientifically important data.

ISBN: 9781392318324Subjects--Topical Terms:

3350019
Computational chemistry.

Data Science in Scanning Probe Microscopy: Advanced Analytics and Machine Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Hudson, Eric.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Scanning probe microscopy (SPM) has allowed researchers to measure materials' structural and functional properties, such as atomic displacements and electronic properties at the nanoscale. Over the past decade, great leaps in the ability to acquire large, high resolution datasets have opened up the possibility of even deeper insights into materials. Unfortunately, these large datasets pose a problem for traditional analysis techniques (and software), necessitating the development of new techniques in order to better understand this new wealth of data.Fortunately, these developments are paralleled by the general rise of big data and the development of machine learning techniques that can help us discover and automate the process of extracting useful information from this data. My thesis research has focused on bringing these techniques to all aspects of SPM usage, from data collection through analysis. In this dissertation I present results from three of these efforts: the improvement of a vibration cancellation system developed in our group via the introduction of machine learning, the classification of SPM images using machine vision, and the creation of a new data analysis software package tailored for large, multidimensional datasets which is highly customizable and eases performance of complex analyses.Each of these results stand on their own in terms of scientific impact - for example, the machine learning approach discussed here enables a roughly factor of two to three improvement over our already uniquely successful vibration cancellation system. However, together they represent something more - a push to bring machine learning techniques into the field of SPM research, where previously only a handful of research groups have reported any attempts, and where all efforts to date have focused on analysis, rather than collection, of data. These results also represent first steps in the development of a "driverless SPM" where the SPM could, on its own, identify, collect, and begin analysis of scientifically important data.
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