{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T08:52:09Z","timestamp":1769935929866,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MCTES","award":["PTDC\/EMEAPL\/1342\/2020"],"award-info":[{"award-number":["PTDC\/EMEAPL\/1342\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Atomic force microscopy (AFM) is a powerful technique to study the nanomechanical properties of a wide range of materials at the piconewton level. AFM force\u2013indentation curves can be fitted with appropriate contact models, enabling the determination of material properties for a given sample. However, the analysis of large datasets comprising thousands of curves using conventional methods presents a time-intensive challenge. As a result, there is an increasing interest in exploring alternative methodologies, such as integrating machine learning (ML) models to streamline and improve the efficiency of this process. In this work, two data-driven regressors were tuned to predict the Young\u2019s modulus and adhesion energy from force\u2013indentation curves of soft samples (Young\u2019s modulus up to 10 kPa). Both models were trained exclusively on synthetic data derived from the contact theories developed by Hertz as well as Johnson, Kendall and Roberts (JKR). The PyTorch library was employed to build and train the models; then, the key hyperparameters were refined by implementing the optimization framework Optuna. The first model was successfully tested with synthetic and experimental curves from AFM nanoindentations, and the second presented promising results on the synthetic data. Our work suggests that experimental data may not be essential for training data-driven models to predict surface properties from AFM nanoindentations. By delivering accurate predictions in a computationally efficient way, our regressors validate the potential of a deep learning approach in exploring AFM nanoindentations and motivate further development of similar strategies to overcome current limitations in AFM postprocessing.<\/jats:p>","DOI":"10.3390\/app14062376","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:14:40Z","timestamp":1710245680000},"page":"2376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8313-2700","authenticated-orcid":false,"given":"Lu\u00eds R. L.","family":"Pacheco","sequence":"first","affiliation":[{"name":"DEMec\u2014Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4310-2915","authenticated-orcid":false,"given":"Jo\u00e3o P. S.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"DEMec\u2014Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3326-6345","authenticated-orcid":false,"given":"Marco P. L.","family":"Parente","sequence":"additional","affiliation":[{"name":"DEMec\u2014Department of Mechanical Engineering, Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"},{"name":"INEGI\u2014Institute of Science and Innovation in Mechanical and Industrial Engineering, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Voigtl\u00e4nder, B. (2019). Atomic Force Microscopy, Springer. [2nd ed.].","DOI":"10.1007\/978-3-030-13654-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1103\/PhysRevLett.56.930","article-title":"Atomic Force Microscope","volume":"56","author":"Binnig","year":"1986","journal-title":"Phys. Rev. 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