[en] Over evolution, organisms develop complex material structures fit to their environments. Based on these time-tested designs, human-engineered bioinspired structures offer exciting possible materials configurations. However, navigating diverse structure spaces for attaining desired properties remains non-trivial. We focus on the hardest biological tissue in humans, tooth enamel, to examine the structure-property relationship. While typical hardness measurements are time consuming and destructive, we propose that artificial intelligence models can predict properties directly and enable high-throughput, non-destructive characterization. We train a deep image regression neural network as a surrogate model and visualize with gradient ascent and saliency maps to identify structural features contributing most to hardness. This model demonstrates improved spatial resolution and sensitivity compared with experimental hardness maps. Using this rapid hardness testing model, a generative adversarial model, and a genetic algorithm that operates in latent space, allows for guided materials design, yielding proposed designs for bioinspired structures with precisely controlled hardness.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Lew, Andrew J.; Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, United States ; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, United States
Stifler, Cayla A.; Department of Physics, University of Wisconsin, Madison, United States
Cantamessa, Astrid ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Tits, Alexandra ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Ruffoni, Davide ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Mécanique des matériaux biologiques et bioinspirés
Gilbert, Pupa U.P.A.; Department of Physics, University of Wisconsin, Madison, United States ; Department of Chemistry, University of Wisconsin, Madison, United States ; Departments of Materials Science and Engineering, Geoscience, University of Wisconsin, Madison, United States ; Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, United States
Buehler, Markus J. ; Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, Cambridge, United States
Language :
English
Title :
Deep learning virtual indenter maps nanoscale hardness rapidly and non-destructively, revealing mechanism and enhancing bioinspired design
This material is based upon work supported by the NSF GRFP under grant no. 1122374 . We acknowledge support by the Office of Naval Research ( N000141612333 and N000141912375 ), AFOSRMURI ( FA9550-15-1-0514 ), and the Army Research Office ( W911NF1920098 ). Related support from the MIT-IBM Watson AI Lab , MIT Quest , and Google Cloud Computing is acknowledged. P.U.P.A.G. received 40% support from the Department of Energy, Basic Energy Science, Chemical Sciences, Geosciences, Biosciences, Geosciences (DOE-BES-CSGB-Geosciences) grant DE-FG02-07ER15899 at University of Wisconsin , 40% support from award FWP-FP00011135 also from DOE-BES-CSGB-Geosciences at Lawrence Berkeley National Laboratory , and 20% support from the National Science Foundation (NSF), Biomaterials grant DMR-2220274 . All PIC maps were acquired at the Advanced Light Source, a US DOE Office of Science User Facility under contract no. DE-AC02-05CH11231.
Callister, W.D., Rethwisch, D.G., Materials Science and Engineering: An Introduction. 7th ed., 2013, John Wiley & Sons, Inc.
Sudharshan Phani, P., Oliver, W.C., A critical assessment of the effect of indentation spacing on the measurement of hardness and modulus using instrumented indentation testing. Mater. Des., 164, 2019, 107563, 10.1016/J.MATDES.2018.107563.
Tanaka, Y., Seino, Y., Hattori, K., Automated Vickers hardness measurement using convolutional neural networks. Int. J. Adv. Manuf. Technol. 109 (2020), 1345–1355, 10.1007/s00170-020-05746-4/Published.
Kranenburg, J.M., Tweedie, C.A., Van Vliet, K.J., Schubert, U.S., Challenges and progress in high-throughput screening of polymer mechanical properties by indentation. Adv. Mater. 21 (2009), 3551–3561, 10.1002/ADMA.200803538.
Hintsala, E.D., Hangen, U., Stauffer, D.D., -Bruker, N., Surfaces, E., Prairie, U., High-throughput nanoindentation for statistical and spatial property determination. J. Miner. Met. Mater. Soc. 70 (2018), 494–503, 10.1007/s11837-018-2752-0.
Constantinides, G., Ravi Chandran, K., Ulm, F.-J., Van Vliet, K.J., Grid indentation analysis of composite microstructure and mechanics: principles and validation. Mater. Sci. Eng., A 430 (2006), 189–202, 10.1016/j.msea.2006.05.125.
Hsu, Y.C., Yu, C.H., Buehler, M.J., Using deep learning to predict fracture patterns in crystalline solids. Matter 3 (2020), 197–211, 10.1016/j.matt.2020.04.019.
Lew, A.J., Yu, C.-H., Hsu, Y.-C., Buehler, M.J., Deep learning model to predict fracture mechanisms of graphene. npj 2D Mater. Appl., 5, 2021, 48, 10.1038/s41699-021-00228-x.
Lew, A.J., Buehler, M.J., A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design. Appl. Phys. Rev., 8, 2021, 041414, 10.1063/5.0057162.
Lew, A.J., Buehler, M.J., Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model. Forces in Mechanics, 5, 2021, 100054, 10.1016/J.FINMEC.2021.100054.
Lew, A.J., Buehler, M.J., DeepBuckle: extracting physical behavior directly from empirical observation for a material agnostic approach to analyze and predict buckling. J. Mech. Phys. Solid., 164, 2022, 104909, 10.1016/J.JMPS.2022.104909.
Craig, R.G., Peyton, F.A., The microhardness of enamel and dentin. J. Dent. Res. 37 (1958), 661–668, 10.1177/00220345580370041301.
Gilbert, P.U.P.A., Young, A., Coppersmith, S.N., Measurement of c-axis angular orientation in calcite (CaCO3) nanocrystals using X-ray absorption spectroscopy. Proc. Natl. Acad. Sci. USA 108 (2011), 11350–11355, 10.1073/pnas.1107917108.
Beniash, E., Stifler, C.A., Sun, C.Y., Jung, G.S., Qin, Z., Buehler, M.J., Gilbert, P.U.P.A., The hidden structure of human enamel. Nat. Commun. 10 (2019), 4383–4413, 10.1038/s41467-019-12185-7.
Cuy, J.L., Mann, A.B., Livi, K.J., Teaford, M.F., Weihs, T.P., Nanoindentation mapping of the mechanical properties of human molar tooth enamel. Arch. Oral Biol. 47 (2002), 281–291, 10.1016/S0003-9969(02)00006-7.
Habelitz, S., Materials engineering by ameloblasts. J. Dent. Res. 94 (2015), 759–767, 10.1177/0022034515577963.
He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. Preprint at arXiv, 2015, 10.48550/arXiv.1512.03385.
Adadi, A., Berrada, M., Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6 (2018), 52138–52160, 10.1109/ACCESS.2018.2870052.
Erhan, D., Bengio, Y., Courville, A., Vincent, P., Visualizing Higher-Layer Features of a Deep Network, 1341, 2009, University of Montreal, 1–13.
Chollet, F., Visualizing what Convnets Learn. 2020, GitHub.
Simonyan, K., Vedaldi, A., Zisserman, A., Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at arXiv, 2014, 10.48550/arXiv.1312.6034.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58 (2020), 82–115, 10.1016/J.INFFUS.2019.12.012.
Holland, J., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. 1975, University of Michigan Press.
Whitley, D., A genetic algorithm tutorial. Stat. Comput. 4 (1994), 65–85, 10.1007/BF00175354.
Chakraborti, N., Genetic algorithms in materials design and processing. Int. Mater. Rev. 49 (2013), 246–260, 10.1179/095066004225021909.
Karras, T., Laine, S., Aila, T., A style-based generator architecture for generative adversarial networks. Preprint at arXiv, 2019, 10.48550/arXiv.1812.04948.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T., Analyzing and improving the image quality of StyleGAN. Preprint at arXiv, 2020, 10.48550/arXiv.1912.04958.
Yang, K.K., Wu, Z., Arnold, F.H., Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16 (2019), 687–694, 10.1038/s41592-019-0496-6.
Gupta, D., Ghafir, S., An overview of methods maintaining diversity in genetic algorithms. International Journal of Emerging Technology and Advanced Engineering, 2, 2012, 56.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. language models are few-shot learners. Preprint at arXiv, 2020, 10.48550/arXiv.2005.14165.
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I., Zero-shot text-to-image generation. Preprint at arXiv, 2021 https://arxiv.org/abs/2102.12092.
Yang, Z., Buehler, M.J., Words to matter: de novo architected materials design using transformer neural networks. Front. Mater., 8, 2021, 417, 10.3389/FMATS.2021.740754/BIBTEX.
Oliver, W.C., Pharr, G.M., An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments. J. Mater. Res. 7 (1992), 1564–1583, 10.1557/JMR.1992.1564.
Wright, L., Ranger - a Synergistic Optimizer. 2019, GitHub https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer.
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J., On the variance of the adaptive learning rate and beyond. Preprint at arXiv, 2019, 10.48550/arxiv.1908.03265.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. TensorFlow: a system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016, 265–283.
TensorFlow 1.x vs TensorFlow 2 - behaviors and APIs. TensorFlow Core, 2022.
