| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000431731 |
| 005 | | 20200224104537 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392318324 |
| 035 | |
▼a (MiAaPQ)AAI13917899 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 530 |
| 100 | 1 |
▼a Dusch, William. |
| 245 | 10 |
▼a Data Science in Scanning Probe Microscopy: Advanced Analytics and Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b The Pennsylvania State University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 158 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 80-12, Section: B. |
| 500 | |
▼a Publisher info.: Dissertation/Thesis. |
| 500 | |
▼a Advisor: Hudson, Eric. |
| 502 | 1 |
▼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. |
| 590 | |
▼a School code: 0176. |
| 650 | 4 |
▼a Computational chemistry. |
| 650 | 4 |
▼a Physics. |
| 650 | 4 |
▼a Condensed matter physics. |
| 690 | |
▼a 0219 |
| 690 | |
▼a 0605 |
| 690 | |
▼a 0611 |
| 710 | 20 |
▼a The Pennsylvania State University.
▼b Physics. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 80-12B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0176 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492596
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |