Interpretable Machine Learning Approach for Identifying the Tip Sharpness in Atomic Force Microscopy

Zaki, Mohd; Kasimuthumaniyan, S.; Sahoo, Sourav; Jayadeva,; Gosvami, Nitya Nand; Krishnan, N. M. Anoop

doi:10.1016/j.scriptamat.2022.114965

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Over the past years, SHAP analysis has successfully been utilized to interrogate various machine learning models in several glass-related studies. 30,[45][46][47] The SHAP analysis involves calculating outcome differences by permuting the factor of interest (between its actual value and its mean value) across all instances under different conditions. By averaging these differences over all instances, we can assess the marginal contribution of the factor of interest (i.e., Shapley value).…”

Section: Shap Analysismentioning

confidence: 99%

Unveiling the Effect of Composition on Nuclear Waste Immobilization Glasses’ Durability by Non-Parametric Machine Learning

Bauchy,

Song,

et al. 2023

Preprint

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Ensuring the long-term chemical durability of glasses is critical for nuclear waste immobilization operations. Durable glasses usually undergo qualification for disposal based on their response to standardized tests such as the product consistency test or the vapor hydration test (VHT). The VHT uses elevated temperature and water vapor to accelerate glass alteration and the formation of secondary phases. Understanding the relationship between glass composition and VHT response is of fundamental and practical interest. However, this relationship is complex, non-linear, and sometimes fairly variable, posing challenges in identifying the distinct effect of individual oxides on VHT response. Here, we leverage a dataset comprising 654 Hanford low-activity waste (LAW) glasses across a wide compositional envelope and employ various machine learning techniques to explore this relationship. We find that Gaussian process regression (GPR), a non-parametric regression method, yields the highest predictive accuracy. By utilizing the trained model, we discern the influence of each oxide on the glasses' VHT response. Moreover, we discuss the trade-off between underfitting and overfitting for extrapolating the material performance in the context of sparse and heterogeneous datasets.

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Section: Shap Analysismentioning

confidence: 99%

Unveiling the Effect of Composition on Nuclear Waste Immobilization Glasses’ Durability by Non-Parametric Machine Learning

Bauchy,

Song,

et al. 2023

Preprint

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“…[1][2][3][4] These include the development of materials specic language models, [5][6][7][8] rule-based systems, [9][10][11][12][13] IE from tables, 8,14,15 and IE from images. [16][17][18][19] The widely varying information expression styles in research papers make the automated MatSci IE a challenging task. Most of the studies have focused on IE in a specic domain; hence, the transferability to different materials is not explored.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the materials tetrahedron: challenges in materials information extraction

Hira,

Zaki,

Sheth

et al. 2024

Digital Discovery

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“…A ML approach based on artificial neural networks (ANNs) is used in [28] to classify bladder cancer cells into different grades, based on cellular mechanical properties obtained with AFM. For an application more related to AFM intrinsic properties, Convolutional neural network (CNN) models have been developed to determine the tip sharpness directly from indentation images [29]. 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).…”

Section: Introductionmentioning

confidence: 99%

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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Interpretable Machine Learning Approach for Identifying the Tip Sharpness in Atomic Force Microscopy

Cited by 7 publications

References 20 publications

Unveiling the Effect of Composition on Nuclear Waste Immobilization Glasses’ Durability by Non-Parametric Machine Learning

Unveiling the Effect of Composition on Nuclear Waste Immobilization Glasses’ Durability by Non-Parametric Machine Learning

Reconstructing the materials tetrahedron: challenges in materials information extraction

Deep Learning Regressors of Surface Properties from Atomic Force Microscopy Nanoindentations

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