Student: Alibek Kaliyev

Project: Accelerated Fitting of Band-Excitation Piezorspone Force Microscopy

View: Research Poster (PDF)

Institution: Lehigh University

Major: Computer Science and Engineering

Advisor: Joshua Agar


For nearly a decade, band-excitation piezoresponse force-based switching spectroscopy (BEPS) has been used to characterize ferroelectric switching and dynamic electromechanical responses of materials with nanoscale resolution. One of the key outputs of this technique is hyperspectral images of piezoelectric hysteresis loops, wherein there are one or more hysteresis loops at every pixel position. The challenge and dedication required to properly analyze data from these experiments have throttled the impact and widespread use of BEPS. To simplify the extraction of information from these datasets, a common approach involves fitting the piezoelectric hysteresis loops to an empirical function to parameterize the loops. This technique is computationally intensive, requiring more than 24 hours to process a single experiment on a single workstation with parallel processing, and is highly dependent on prior estimates.

My goal was to develop a fully unsupervised approach based on deep recurrent neural networks in the form of an autoencoder which is able to learn a sparse and thus interpretable latent space of piezoelectric hysteresis loops, revealing detailed physical insight that will allude to results from other analysis techniques. We developed generalized pre-trained models which can conduct feature extraction from piezoelectric hysteresis loops with minimal or potentially no training.

Using this approach, it is possible to deploy these methods for real-time analysis of BEPS, thus enabling experimentalists to improve their experimental efficiency and extract more information from these experiments.

While the work focuses on developing models and benchmarking their efficacy in BEPS, this methodology could be adapted to other spectroscopic imaging techniques.


Alibek Kaliyev

About Alibek Kaliyev

Alibek Kaliyev is a first-year international student majoring in Computer Science and Business at Lehigh University. He is an undergraduate researcher in the Multifunctional Materials and Machine Learning Group, Data for Impact Fellow at Creative Inquiry and Mountaintop Initiative, and a course assistant for the Department of Computer Science and Engineering. Currently, he is working on accelerating the fitting process of band-excitation piezoresponse force microscopy by applying deep learning methods. Previously, Alibek was working as a Social Media Marketing Intern in Cisco’s Corporate Responsibility Team to promote Cisco Networking Academy’s products and services that help students around the world to develop an interest in the tech industry and acquire technical skills. Alibek has a passion for becoming an AI engineer by combining his knowledge from engineering, science and business fields. In his free time, Alibek loves to do karate, which he has been doing for 15 years.