NCSI: Local Conductivity Mapping using Kelvin Probe Force Microscopy Impedance Modeling

Status	Completed
Mentor Name	Ryan Dwyer
Mentor's XSEDE Affiliation	Startup Allocation / EMPOWER Mentor
Mentor Has Been in XSEDE Community	1-2 years
Project Title	Local Conductivity Mapping using Kelvin Probe Force Microscopy Impedance Modeling
Summary	In Kelvin probe force microscopy (KPFM), electrostatic forces between a nanometer-scale tip and a sample surface can produce nanometer-resolution images; however, linking the raw experimental data to specific sample electrical properties can be challenging. During a summer XSEDE EMPOWER project, simulations combining tip-sample electrostatic modeling with a Langragian impedance model revealed new experimental conditions that best probe specific sample electrical properties such as conductivity and dielectric constant. To test these predictions, the student will write Python code to perform and analyze KPFM and/or electrochemistry experiments; these new experiments and analyses will be shared with other researchers via open-source Python code and a web interface for those without programming experience.
Job Description	Using the codebase and scripts developed this summer, the student will simulate force microscopy experiments for new semiconductor materials. These simulations will be used to choose the best experimental conditions for Kelvin probe force microscopy and electrochemistry experiments. The student will write Python scripts to perform experiments and/or analyze the experimental results. The force microscopy simulations and Lagrangian impedance model will be used to fit the measured data to relevant sample parameters. Using these results, the student will confirm whether the predicted experimental conditions best distinguish different sample properties. Depending on the experimental results, additional modeling of the semiconductor materials using computational chemistry software (molecular dynamics using LAMMPS or SciFi; DFT using Gaussian or ORCA) could be useful to draw additional conclusions about the materials from the KPFM and/or electrochemistry data. The student will make these results accessible to the broader scientific community by posting one or more of these data analysis procedures publicly on the web using the Streamlit Python package and XSEDE Jetstream resources, which will enable other researchers to apply this analysis to their own images with no coding on their part. For those with programming experience, the source code will also be posted publicly on GitHub. The student will summarize their results in a technical report and an oral or poster presentation. The technical report and presentation will be used to create an abstract suitable for submission to a scientific conference and prepare a manuscript for publication.
Computational Resources	We have access to low-cost computational resources through the Ohio Supercomputer Center; those will be the primary computing resources used. The web interface to make these force microscopy analysis tools available to the broader community will use XSEDE Jetstream (via a startup or research allocation). XSEDE Jetstream is uniquely suited to host the force microscopy analysis tools because we can install all the necessary complied Fortran programs and Python packages used in the analysis and make the result available publicly on the web.
Contribution to Community	A student researcher will develop skills in Python programming, computational chemistry, data visualization, and high-performance computing. These skills will allow the student to contribute to XSEDE and its mission. The student researcher will also continue to develop skills in scientific writing, communication, and presentation. These skills are also essential to ensure that high-performance computing reaches the widest possible audience. Scientifically, force microscopy is used by a large community of scientists who are often excluded from the latest advances in theory and technique because they lack the time and/or expertise to learn and use these new approaches. By sharing our key results through a web interface that requires no coding, this project will make recent advances in force microscopy modeling more accessible to other researchers and users in the field. XSEDE plays a critical role in accomplishing this goal since modeling the experiment requires combining compiled Fortran programs and a Python codebase.
Position Type	Apprentice
Training Plan	This summer the student learned the basics of Python programming, the scientific Python ecosystem (numpy, scipy, matplotlib, etc.), and high-performance computing (writing Python and/or shell scripts to submit batches of simulations to the Ohio Supercomputer Center). This previous training will enable the student to work relatively independently extending the analyses developed this summer to new classes of semiconductor materials. To continue this work and analyze experimental results, the student will need additional experience with Python debugging, testing, version control and the scientific Python ecosystem. Resources from the Ohio Supercomputer Center, XSEDE, Software Carpentry, video tutorials, and Python programming books will be used along with one-on-one mentoring meetings to further develop these skills as needed. Depending on what the experimental results indicate, additional computational chemistry programs may be used to analyze these semiconductor materials. If needed, training materials for these resources can be modified from an upper-level chemistry elective I teach, CHE 443 Computational Chemistry and Spectroscopy.
Student Prerequisites/Conditions/Qualifications	The student should have proficiency in Python, the Streamlit framework for creating simple Python web applications, knowledge of bash and supercomputing to submit batch jobs to the Ohio Supercomputer Center, and an understanding of scanned probe microscopy.
Duration	Semester
Start Date	08/23/2021
End Date	12/10/2021