Machine learning framework for determination of elastic modulus without contact model fitting

Nguyen, Linh Thi Phuong; Liu, Bernard Haochih

doi:10.1016/j.ijsolstr.2022.111976

Cited by 4 publications

(4 citation statements)

References 57 publications

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“…In addition, there is also the chance of the model overfitting to noise in the data. In common ML applications, the number of instances in a dataset usually goes from a few thousand to hundreds of thousands, and it is no different when applied to AFM frameworks [31,46]. Thus, to ensure a robust initial model without excessively compromising computational costs, an initial dataset consisting of 40,000 curves was created.…”

Section: Synthetic Nanoindentation Curvesmentioning

confidence: 99%

“…A different study [30] has presented a quasi-recurrent neural network to identify the coupling of vibrating modes in dynamic AFM (intermittent contact between probe and sample). At last, ML regression models were used to predict the elastic modulus based on nanoindentation curves, without having to fit them with a contact model, in the work presented in [31], where the ML models were trained with experimental F-I curves from AFM analyses. This last work shares some similarities with the current project, in the sense that they both focus on determining the sample's stiffness without contact model fitting.…”

Section: Introductionmentioning

confidence: 99%

“…This last work shares some similarities with the current project, in the sense that they both focus on determining the sample's stiffness without contact model fitting. Nevertheless, the work in [31] does not cover the development of deep learning strategies and it requires experimental data to train the models, unlike the current framework.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations

Pacheco,

Ferreira,

Parente

2024

Applied Sciences

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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–indentation 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’s modulus and adhesion energy from force–indentation curves of soft samples (Young’s 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.

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Section: Synthetic Nanoindentation Curvesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations

Pacheco,

Ferreira,

Parente

2024

Applied Sciences

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“…To address this problem, three different contact models were considered, distinguished by whether or not they allow tip-surface adhesion and whether the tip is more spherical or spheroconical. 112 These were used to train the ML model by finding the most appropriate model for the curve and subsequently the associated elastic modulus. These fit values were associated with seven different characteristic (dimensionless) features of each force curve.…”

Section: Nanowires and Nanotubesmentioning

confidence: 99%

Some considerations in nanoindentation measurement and analysis by atomic force microscopy

Rosenhek-Goldian,

Cohen

2023

Journal of Vacuum Science &Amp; Technology A

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Atomic force microscopy is highly suited for characterizing morphology and physical properties of nanoscale objects. The application of this technique to nanomechanical studies is, therefore, exploited in a wide range of fields from life sciences to materials science and from miniature devices to sensors. Although performing a mechanical measurement can be straightforward and accessible to novice users, obtaining meaningful results requires knowledge and experience not always evident in standard instrumental software modules. In this paper, we provide a basic guide to proper protocols for the measurement and analysis of force curves and related atomic force microscopic techniques. Looking forward, we also survey the budding application of machine learning in this discipline.

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Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis

O’Dowling,

Rodriguez,

Gallagher

et al. 2024

Computational and Structural Biotechnology Journal

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Machine learning framework for determination of elastic modulus without contact model fitting

Cited by 4 publications

References 57 publications

Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations

Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations

Some considerations in nanoindentation measurement and analysis by atomic force microscopy

Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis

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