Raw data of scanning electron microscopy (SEM), atomic force microscopy (AFM), force spectroscopy, data analysis and plotting, optical microscopy, and finite element simulations (FEA) for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Imported raw data to MATLAB and saved MATLAB data were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) FEA results were saved as text files, .txt files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. Read me file.docx More Explanations of analysis in docx format. Figure 1 Figure 1  panel b.jpg (5.5 MB) Optical micrograph (JPEG format) Figure 1  panel c  AFM Raw Data.gwy (8.0 MB) AFM raw data (Gwyddion format) Figure 1  panel e  P0_Forcecurve_raw_data.txt (3 KB) Raw forcedisplacement data at P0 (text format) Figure 1  panel e  Px_Forcecurve_raw_data.txt (3 KB) Raw forcedisplacement data at Px (text format) Figure 1  panel e  Py_Forcecurve_raw_data.txt (3 KB) Raw forcedisplacement data at Py (text format) Figure 1  panel e  P0_simulation_raw_data.txt (12 KB) FEA simulated forcedistance data at P0 (text format) Figure 1  panel e  Px_simulation_raw_data.txt (12 KB) FEA simulated forcedistance data at Px (text format) Figure 1  panel e  Py_simulation_raw_data.txt (12 KB) FEA simulated forcedistance data at Py (text format) Figure 1  panel e  FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 1  panel e  FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 1  panel e  FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 1  panel e  FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 1  panel e  FCimport.m (7 KB) MATLAB code to import raw forcedisplacement data into MATLAB (matlab .m format) Figure 1  panel e  FCForceDist.m (2 KB) MATLAB code to convert raw forcedisplacement data into forcedistance data (matlab .m format) Figure 1  panel e  Figure 1 Panel e  data.mat (6 KB) MATALB struct data file for calibrated forcedistance data at all indentation points (matlab .mat format) Figure 1  panel e  Panel_e_MatlabCode.m (6 KB) MATALB code for plotting experimental and simulated force curves in panel e (matlab .m format) Figure 1  panel e  Read me file  force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1  panel e  Read me file  lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 2 Figure 2  panel a  MATLAB data.mat (2.6 KB) MATALB data file for simulated data (matlab .mat format) Figure 2  panel b  MATLAB data.mat (2.4 KB) MATALB data file for simulated data (matlab .mat format) Figure 2  panel a  simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2  panel b  simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2  panel ab  MATLABcode.m (1.0 KB) MATALB code for plotting panel a b figures (matlab .m format) Figure 2  panel c  Degree of Anisotropy datacode.m (1.0 KB) MATALB code for plotting panel c graph (matlab .m format) Figure 3 Figure 3  panel a  App_curve_1_raw_data.txt (35 KB) Raw forcedisplacement data approach curve 1 (text format) Figure 3  panel a  App_curve_2_raw_data.txt (34 KB) Raw forcedisplacement data approach curve 2 (text format) Figure 3  panel a  App_curve_3_raw_data.txt (34 KB) Raw forcedisplacement data approach curve 3 (text format) Figure 3  panel a  App_curve_4_raw_data.txt (34 KB) Raw forcedisplacement data approach curve 4 (text format) Figure 3  panel a  Ret_curve_1_raw_data.txt (35 KB) Raw forcedisplacement data of retract curve 1 (text format) Figure 3  panel a  Ret_curve_2_raw_data.txt (35 KB) Raw forcedisplacement data of retract curve 2 (text format) Figure 3  panel a  Ret_curve_3_raw_data.txt (35 KB) Raw forcedisplacement data of retract curve 3 (text format) Figure 3  panel a  Simulation_raw_datapart 1.txt (43 KB) simulated forcedisplacement data of part 1 (text format) Figure 3  panel a  Simulation_raw_datapart 2.txt (43 KB) simulated forcedisplacement data of part 2 (text format) Figure 3  panel a  FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 3  panel a  FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 3  panel a  FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 3  panel a  FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 3  panel a  FCimport.m (7 KB) MATLAB code to import raw forcedisplacement data into MATLAB (matlab .m format) Figure 3  panel a  FCForceDist.m (2 KB) MATLAB code to convert raw forcedisplacement data into forcedistance data (matlab .m format) Figure 1  panel e  Read me file  force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1  panel e  Read me file  lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 3  panel b  SEM Raw Data.tiff (9 KB) SEM raw image of broken silk membrane due to extreme indentation (.tiff format)
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This content will become publicly available on October 1, 2024
AIdente: an open machine learning based tool to interpret nanoindentation data of soft tissues and materials
Nanoindentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nanoindentation allows mechanical interrogation where traditional methods may fail. However, because nanoindentation does not yield a homogeneous deformation field, interpreting the resulting load–displacement curves is nontrivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load–displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neoHookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nanoindentation on soft materials.
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 NSFPAR ID:
 10451286
 Date Published:
 Journal Name:
 Soft Matter
 ISSN:
 1744683X
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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