Machine learning for analyzing atomic force microscopy (AFM) images generated from polymer blends

Paruchuri, Aanish; Wang, Yunfei; Gu, Xiaodan; Jayaraman, Arthi

doi:10.1039/D4DD00215F

Title: Machine learning for analyzing atomic force microscopy (AFM) images generated from polymer blends

Journal Article · 04 December 2024 · Digital Discovery

DOI: https://doi.org/10.1039/D4DD00215F · OSTI ID:2475705

Paruchuri, Aanish ^[1];

^[2];

^[3]

Master of Science in Data Science Program, University of Delaware, Newark, DE 19713, USA
School of Polymer Science and Engineering, University of Southern Mississippi, 118 College Drive, #5050, Hattiesburg, MS 39406, USA
Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St, Newark, DE 19713, USA, Department of Materials Science and Engineering, University of Delaware, Newark, DE 19713, USA, Data Science Institute, University of Delaware, Newark, DE, 19713, USA

In this paper, we present a new machine learning (ML) workflow with unsupervised learning techniques to identify domains within atomic force microscopy (AFM) images obtained from polymer films.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0024432; AC02-05CH11231

OSTI ID:: 2475705

Journal Information:: Digital Discovery, Journal Name: Digital Discovery Journal Issue: 12 Vol. 3; ISSN DDIIAI; ISSN 2635-098X

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

References (80)

Highly Deformable Rigid Glassy Conjugated Polymeric Thin Films Wang, Yunfei; Zhang, Song; Freychet, Guillaume Advanced Functional Materials, Vol. 33, Issue 50 https://doi.org/10.1002/adfm.202306576	journal	August 2023
Block Copolymer Nanocomposites: Perspectives for Tailored Functional Materials Bockstaller, M. R.; Mickiewicz, R. A.; Thomas, E. L. Advanced Materials, Vol. 17, Issue 11, p. 1331-1349 https://doi.org/10.1002/adma.200500167	journal	June 2005
General Resolution Enhancement Method in Atomic Force Microscopy Using Deep Learning Liu, Yue; Sun, Qiaomei; Lu, Wanheng Advanced Theory and Simulations, Vol. 2, Issue 2 https://doi.org/10.1002/adts.201800137	journal	September 2018
New Opportunity: Machine Learning for Polymer Materials Design and Discovery Xu, Pengcheng; Chen, Huimin; Li, Minjie Advanced Theory and Simulations, Vol. 5, Issue 5 https://doi.org/10.1002/adts.202100565	journal	February 2022
Predicting AFM Topography from Optical Microscope Images Using Deep‐Learning Jeong, Jaewoo; Kim, Taeyeong; Lee, Bong Jae Advanced Intelligent Systems, Vol. 5, Issue 1 https://doi.org/10.1002/aisy.202200317	journal	December 2022
Accelerating AFM Characterization via Deep‐Learning‐Based Image Super‐Resolution Kim, Young‐Joo; Lim, Jaekyung; Kim, Do‐Nyun Small, Vol. 18, Issue 3 https://doi.org/10.1002/smll.202103779	journal	November 2021
A novel transfer learning for recognition of overlapping nano object Han, Yuexing; Liu, Yuhong; Wang, Bing Neural Computing and Applications, Vol. 34, Issue 7 https://doi.org/10.1007/s00521-021-06731-y	journal	January 2022
Data and Machine Learning in Polymer Science Li, Yun-Qi; Jiang, Ying; Wang, Li-Quan Chinese Journal of Polymer Science, Vol. 41, Issue 9 https://doi.org/10.1007/s10118-022-2868-0	journal	October 2022
ImageNet Large Scale Visual Recognition Challenge Russakovsky, Olga; Deng, Jia; Su, Hao International Journal of Computer Vision, Vol. 115, Issue 3 https://doi.org/10.1007/s11263-015-0816-y	journal	April 2015
Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective Baskaran, Arun; Kautz, Elizabeth J.; Chowdhary, Aritra JOM, Vol. 73, Issue 11 https://doi.org/10.1007/s11837-021-04805-9	journal	September 2021
Texture characterization using robust statistics Muzzolini, Russell; Yang, Yee-Hong; Pierson, Roger Pattern Recognition, Vol. 27, Issue 1 https://doi.org/10.1016/0031-3203(94)90022-1	journal	January 1994
A fast and adaptive method to estimate texture statistics by the spatial gray level dependence matrix (SGLDM) for texture image segmentation Lee, Jeong Hwan; Lee, Nam II; Kim, Seong Dae Pattern Recognition Letters, Vol. 13, Issue 4 https://doi.org/10.1016/0167-8655(92)90079-f	journal	April 1992
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning Kermany, Daniel S.; Goldbaum, Michael; Cai, Wenjia Cell, Vol. 172, Issue 5 https://doi.org/10.1016/j.cell.2018.02.010	journal	February 2018
Characterisation of carbon fibre-reinforced polymer composites through radon-transform analysis of complex eddy-current data Hughes, R. R.; Drinkwater, B. W.; Smith, R. A. Composites Part B: Engineering, Vol. 148 https://doi.org/10.1016/j.compositesb.2018.05.007	journal	September 2018
Comparative study of deep learning algorithms for atomic force microscopy image denoising Jung, Hoichan; Han, Giwoong; Jung, Seong Jun Micron, Vol. 161 https://doi.org/10.1016/j.micron.2022.103332	journal	October 2022
Deep learning analysis on microscopic imaging in materials science Ge, M.; Su, F.; Zhao, Z. Materials Today Nano, Vol. 11 https://doi.org/10.1016/j.mtnano.2020.100087	journal	August 2020
Current issues in research on structure–property relationships in polymer nanocomposites Jancar, J.; Douglas, J. F.; Starr, F. W. Polymer, Vol. 51, Issue 15 https://doi.org/10.1016/j.polymer.2010.04.074	journal	July 2010
Deep Learning based Feature Extraction for Texture Classification Simon, Philomina; V., Uma Procedia Computer Science, Vol. 171 https://doi.org/10.1016/j.procs.2020.04.180	journal	January 2020
Polymer blends and composites from renewable resources Yu, Long; Dean, Katherine; Li, Lin Progress in Polymer Science, Vol. 31, Issue 6, p. 576-602 https://doi.org/10.1016/j.progpolymsci.2006.03.002	journal	June 2006
Directing the self-assembly of block copolymers Darling, S. B. Progress in Polymer Science, Vol. 32, Issue 10 https://doi.org/10.1016/j.progpolymsci.2007.05.004	journal	October 2007
Multiscale modeling and simulation of polymer nanocomposites Zeng, Q. H.; Yu, A. B.; Lu, G. Q. Progress in Polymer Science, Vol. 33, Issue 2 https://doi.org/10.1016/j.progpolymsci.2007.09.002	journal	February 2008
Ordering in thin films of block copolymers: Fundamentals to potential applications Hamley, I. W. Progress in Polymer Science, Vol. 34, Issue 11 https://doi.org/10.1016/j.progpolymsci.2009.06.003	journal	November 2009
Current research on the blends of natural and synthetic polymers as new biomaterials: Review Sionkowska, Alina Progress in Polymer Science, Vol. 36, Issue 9 https://doi.org/10.1016/j.progpolymsci.2011.05.003	journal	September 2011
Recent advance in research on halloysite nanotubes-polymer nanocomposite Liu, Mingxian; Jia, Zhixin; Jia, Demin Progress in Polymer Science, Vol. 39, Issue 8 https://doi.org/10.1016/j.progpolymsci.2014.04.004	journal	August 2014
Block copolymer–nanoparticle composites: Structure, functional properties, and processing Sarkar, Biswajit; Alexandridis, Paschalis Progress in Polymer Science, Vol. 40 https://doi.org/10.1016/j.progpolymsci.2014.10.009	journal	January 2015
Polymer nanocomposites from modified clays: Recent advances and challenges Kotal, Moumita; Bhowmick, Anil K. Progress in Polymer Science, Vol. 51 https://doi.org/10.1016/j.progpolymsci.2015.10.001	journal	December 2015
Recent progress in polymer/two-dimensional nanosheets composites with novel performances Li, Jialong; Liu, Xiaoxu; Feng, Yu Progress in Polymer Science, Vol. 126 https://doi.org/10.1016/j.progpolymsci.2022.101505	journal	March 2022
Machine learning for analyses and automation of structural characterization of polymer materials Lu, Shizhao; Jayaraman, Arthi Progress in Polymer Science, Vol. 153 https://doi.org/10.1016/j.progpolymsci.2024.101828	journal	June 2024
Wavelet transform-based locally orderless images for texture segmentation Bashar, M. K.; Matsumoto, T.; Ohnishi, N. Pattern Recognition Letters, Vol. 24, Issue 15 https://doi.org/10.1016/s0167-8655(03)00107-7	journal	November 2003
Data-Driven Science and Engineering Brunton, Steven L.; Kutz, J. Nathan https://doi.org/10.1017/9781108380690	book	February 2019
Nanowire Detection in AFM Images Using Deep Learning Bai, Huitian; Wu, Sen Microscopy and Microanalysis, Vol. 27, Issue 1 https://doi.org/10.1017/s143192762002468x	journal	November 2020
Advances in Atomic Force Microscopy for Probing Polymer Structure and Properties Wang, Dong; Russell, Thomas P. Macromolecules, Vol. 51, Issue 1 https://doi.org/10.1021/acs.macromol.7b01459	journal	October 2017
Modeling and Simulations of Polymers: A Roadmap Gartner, Thomas E.; Jayaraman, Arthi Macromolecules, Vol. 52, Issue 3 https://doi.org/10.1021/acs.macromol.8b01836	journal	January 2019
Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning Patel, Roshan A.; Webb, Michael A. ACS Applied Bio Materials https://doi.org/10.1021/acsabm.2c00962	journal	January 2023
Predicting Materials Properties with Little Data Using Shotgun Transfer Learning Yamada, Hironao; Liu, Chang; Wu, Stephen ACS Central Science, Vol. 5, Issue 10 https://doi.org/10.1021/acscentsci.9b00804	journal	September 2019
Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy Kalinin, Sergei V.; Ziatdinov, Maxim; Hinkle, Jacob ACS Nano, Vol. 15, Issue 8 https://doi.org/10.1021/acsnano.1c02104	journal	July 2021
Polymer Nanocomposites with Prescribed Morphology: Going beyond Nanoparticle-Filled Polymers Vaia, Richard A.; Maguire, John F. Chemistry of Materials, Vol. 19, Issue 11 https://doi.org/10.1021/cm062693+	journal	April 2007
Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques Lu, Shizhao; Jayaraman, Arthi JACS Au, Vol. 3, Issue 9 https://doi.org/10.1021/jacsau.3c00275	journal	July 2023
Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D) Akepati, Sri Vishnuvardhan Reddy; Gupta, Nitant; Jayaraman, Arthi JACS Au, Vol. 4, Issue 4 https://doi.org/10.1021/jacsau.4c00068	journal	March 2024
Polymer Nanocomposites Containing Carbon Nanotubes Moniruzzaman, Mohammad; Winey, Karen I. Macromolecules, Vol. 39, Issue 16 https://doi.org/10.1021/ma060733p	journal	August 2006
Functional Polymer Nanocomposites Enhanced by Nanorods Hore, Michael J. A.; Composto, Russell J. Macromolecules, Vol. 47, Issue 3 https://doi.org/10.1021/ma402179w	journal	December 2013
Recent advances and applications of deep learning methods in materials science Choudhary, Kamal; DeCost, Brian; Chen, Chi npj Computational Materials, Vol. 8, Issue 1 https://doi.org/10.1038/s41524-022-00734-6	journal	April 2022
Machine learning for automated experimentation in scanning transmission electron microscopy Kalinin, Sergei V.; Mukherjee, Debangshu; Roccapriore, Kevin npj Computational Materials, Vol. 9, Issue 1 https://doi.org/10.1038/s41524-023-01142-0	journal	December 2023
Towards data-driven next-generation transmission electron microscopy Spurgeon, Steven R.; Ophus, Colin; Jones, Lewys Nature Materials https://doi.org/10.1038/s41563-020-00833-z	journal	October 2020
Block copolymer based composition and morphology control in nanostructured hybrid materials for energy conversion and storage: solar cells, batteries, and fuel cells Orilall, M. Christopher; Wiesner, Ulrich Chem. Soc. Rev., Vol. 40, Issue 2 https://doi.org/10.1039/C0CS00034E	journal	January 2011
Self-assembly of block copolymers Mai, Yiyong; Eisenberg, Adi Chemical Society Reviews, Vol. 41, Issue 18 https://doi.org/10.1039/C2CS35115C	journal	January 2012
Beyond native block copolymer morphologies Doerk, Gregory S.; Yager, Kevin G. Molecular Systems Design & Engineering, Vol. 2, Issue 5 https://doi.org/10.1039/C7ME00069C	journal	January 2017
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook Botifoll, Marc; Pinto-Huguet, Ivan; Arbiol, Jordi Nanoscale Horizons, Vol. 7, Issue 12 https://doi.org/10.1039/D2NH00377E	journal	January 2022
Asymmetric side-chain engineering in semiconducting polymers: a platform for greener processing and post-functionalization of organic electronics Mooney, Madison; Nyayachavadi, Audithya; Awada, Angela Polymer Chemistry, Vol. 14, Issue 5 https://doi.org/10.1039/D2PY01244H	journal	January 2023
Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry Anker, Andy S.; Butler, Keith T.; Selvan, Raghavendra Chemical Science, Vol. 14, Issue 48 https://doi.org/10.1039/D3SC05081E	journal	January 2023
Theory and simulation studies of effective interactions, phase behavior and morphology in polymer nanocomposites Ganesan, Venkat; Jayaraman, Arthi Soft Matter, Vol. 10, Issue 1 https://doi.org/10.1039/c3sm51864g	journal	January 2014
Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images Lu, Shizhao; Montz, Brian; Emrick, Todd Digital Discovery, Vol. 1, Issue 6 https://doi.org/10.1039/d2dd00066k	journal	January 2022
Wavelets for texture analysis, an overview Livens, S. 6th International Conference on Image Processing and its Applications https://doi.org/10.1049/cp:19970958	conference	January 1997
Perspective: Outstanding theoretical questions in polymer-nanoparticle hybrids Kumar, Sanat K.; Ganesan, Venkat; Riggleman, Robert A. The Journal of Chemical Physics, Vol. 147, Issue 2 https://doi.org/10.1063/1.4990501	journal	July 2017
Deep-learning-based nanowire detection in AFM images for automated nanomanipulation Bai, Huitian; Wu, Sen Nanotechnology and Precision Engineering, Vol. 4, Issue 1 https://doi.org/10.1063/10.0003218	journal	January 2021
Machine learning on neutron and x-ray scattering and spectroscopies Chen, Zhantao; Andrejevic, Nina; Drucker, Nathan C. Chemical Physics Reviews, Vol. 2, Issue 3 https://doi.org/10.1063/5.0049111	journal	September 2021
Image-based machine learning for materials science Zhang, Lei; Shao, Shaofeng Journal of Applied Physics, Vol. 132, Issue 10 https://doi.org/10.1063/5.0087381	journal	September 2022
Autonomous x-ray scattering Yager, Kevin G.; Majewski, Pawel W.; Noack, Marcus M. Nanotechnology, Vol. 34, Issue 32 https://doi.org/10.1088/1361-6528/acd25a	journal	May 2023
Rotation-invariant texture analysis using Radon and Fourier transforms Xiao, Song-Shan; Wu, Yong-Xing Journal of Physics: Conference Series, Vol. 48 https://doi.org/10.1088/1742-6596/48/1/268	journal	July 2007
Applications of deep learning in electron microscopy Treder, Kevin P.; Huang, Chen; Kim, Judy S. Microscopy, Vol. 71, Issue Supplement_1 https://doi.org/10.1093/jmicro/dfab043	journal	February 2022
Recognition of polymer configurations by unsupervised learning Xu, Xin; Wei, Qianshi; Li, Huaping Physical Review E, Vol. 99, Issue 4 https://doi.org/10.1103/PhysRevE.99.043307	journal	April 2019
Discrete Cosine Transform Ahmed, N.; Natarajan, T.; Rao, K. R. IEEE Transactions on Computers, Vol. C-23, Issue 1 https://doi.org/10.1109/T-C.1974.223784	journal	January 1974
Radon transform orientation estimation for rotation invariant texture analysis Jafari-Khouzani, K.; Soltanian-Zadeh, H. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, Issue 6 https://doi.org/10.1109/TPAMI.2005.126	journal	June 2005
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 40, Issue 4 https://doi.org/10.1109/TPAMI.2017.2699184	journal	April 2018
AFM Imaging Defect Detection and Classification with Artificial Intelligence and Deep Learning Zhang, Juntao; Ren, Juan; Hu, Shuiqing 2023 IEEE 23rd International Conference on Nanotechnology (NANO) https://doi.org/10.1109/nano58406.2023.10231258	conference	July 2023
Deep-Learning-Based Automated Morphology Analysis With Atomic Force Microscopy Chang, Yingao; Liu, Zhiang; Wu, Yinan IEEE Transactions on Automation Science and Engineering, Vol. 21, Issue 4 https://doi.org/10.1109/tase.2023.3346887	journal	October 2024
Nanoparticle Polymer Composites: Where Two Small Worlds Meet Balazs, A. C.; Emrick, T.; Russell, T. P. Science, Vol. 314, Issue 5802, p. 1107-1110 https://doi.org/10.1126/science.1130557	journal	November 2006
Nanocomposites: Structure, Phase Behavior, and Properties Kumar, Sanat K.; Krishnamoorti, Ramanan Annual Review of Chemical and Biomolecular Engineering, Vol. 1, Issue 1 https://doi.org/10.1146/annurev-chembioeng-073009-100856	journal	June 2010
Machine Learning–Assisted Design of Material Properties Kadulkar, Sanket; Sherman, Zachary M.; Ganesan, Venkat Annual Review of Chemical and Biomolecular Engineering, Vol. 13, Issue 1 https://doi.org/10.1146/annurev-chembioeng-092220-024340	journal	June 2022
Machine Learning for the Discovery, Design, and Engineering of Materials Duan, Chenru; Nandy, Aditya; Kulik, Heather J. Annual Review of Chemical and Biomolecular Engineering, Vol. 13, Issue 1 https://doi.org/10.1146/annurev-chembioeng-092320-120230	journal	June 2022
Opportunities and Challenges for Machine Learning in Materials Science Morgan, Dane; Jacobs, Ryan Annual Review of Materials Research, Vol. 50, Issue 1 https://doi.org/10.1146/annurev-matsci-070218-010015	journal	July 2020
Block Copolymer Thin Films: Physics and Applications Fasolka, Michael J.; Mayes, Anne M. Annual Review of Materials Research, Vol. 31, Issue 1, p. 323-355 https://doi.org/10.1146/annurev.matsci.31.1.323	journal	August 2001
Towards a guideline for evaluation metrics in medical image segmentation Müller, Dominik; Soto-Rey, Iñaki; Kramer, Frank BMC Research Notes, Vol. 15, Issue 1 https://doi.org/10.1186/s13104-022-06096-y	journal	June 2022
Transfer learning: a friendly introduction Hosna, Asmaul; Merry, Ethel; Gyalmo, Jigmey Journal of Big Data, Vol. 9, Issue 1 https://doi.org/10.1186/s40537-022-00652-w	journal	October 2022
Deep learning to establish structure property relationships of impact copolymers from AFM phase images Yablon, Dalia; Chakraborty, Ishita; Passino, Hillary MRS Communications, Vol. 11, Issue 6 https://doi.org/10.1557/s43579-021-00103-2	journal	October 2021
PyWavelets: A Python package for wavelet analysis Lee, Gregory; Gommers, Ralf; Waselewski, Filip Journal of Open Source Software, Vol. 4, Issue 36 https://doi.org/10.21105/joss.01237	journal	April 2019
PoreSpy: A Python Toolkit for Quantitative Analysis of Porous Media Images Gostick, Jeff; Khan, Zohaib; Tranter, Thomas Journal of Open Source Software, Vol. 4, Issue 37 https://doi.org/10.21105/joss.01296	journal	May 2019
Gwyddion: an open-source software for SPM data analysis Nečas, David; Klapetek, Petr Open Physics, Vol. 10, Issue 1 https://doi.org/10.2478/s11534-011-0096-2	journal	January 2012
A Deep Learning Approach for Molecular Classification Based on AFM Images Carracedo-Cosme, Jaime; Romero-Muñiz, Carlos; Pérez, Rubén Nanomaterials, Vol. 11, Issue 7 https://doi.org/10.3390/nano11071658	journal	June 2021
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation Wu, Huikai; Zhang, Junge; Huang, Kaiqi arXiv https://doi.org/10.48550/arXiv.1903.11816	preprint	January 2019

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