Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
Open accessAbstract
A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ML is applied to improve AM processes through failure detection or material design.
“ML is applied to improve those processes, for example through failure detection [722,730] or material design [309].”
From Tackling climate change with machine learning (2023) · cites this paper as [309]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Bioinspired structural materials
Wegst, U. G. K., Bai, H., Saiz, E., Tomsia, A. P., & Ritchie, R. O. (2014) · Nature Materials · doi:10.1038/nmat4089 · Cited by 4,735 · see citing works
The conflicts between strength and toughness
Ritchie, R. O. (2011) · Nature Materials · doi:10.1038/nmat3115 · Cited by 3,989 · see citing works
Selection of relevant features and examples in machine learning
Blum, A. L., & Pat Langley, B. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00063-5 · Cited by 3,294 · see citing works
Nature’s hierarchical materials
Fratzl, P., & Weinkamer, R. (2007) · Progress in Materials Science · doi:10.1016/j.pmatsci.2007.06.001 · Cited by 2,810 · see citing works
The status, challenges, and future of additive manufacturing in engineering
Gao, W., Zhang, Y., Ramanujan, D., Karthik Ramani, A., Chen, Y., Williams, C. B., Wang, C. C. L., Shin, Y. C., Zhang, S., & Zavattieri, P. D. (2015) · Computer-Aided Design · doi:10.1016/j.cad.2015.04.001 · Cited by 2,638 · see citing works
(2013) · Springer eBooks · Cited by 2,625 · see citing works
Face recognition by independent component analysis
Bartlett, M. S., Movellan, J. R., & B, T. J. S. A. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.804287 · Cited by 1,931 · see citing works
Bendsøe, M. P., & Sigmund, O. (2004) · Springer eBooks · doi:10.1007/978-3-662-05086-6 · Cited by 1,790 · see citing works
3D‐Printing of Lightweight Cellular Composites
Compton, B. G., & Lewis, J. A. (2014) · Advanced Materials · doi:10.1002/adma.201401804 · Cited by 1,625 · see citing works
Biomimetism and bioinspiration as tools for the design of innovative materials and systems
Sánchez, C., Arribart, H., & Giraud‐Guille, M. (2005) · Nature Materials · doi:10.1038/nmat1339 · Cited by 1,457 · see citing works
Agrawal, A., & Choudhary, A. (2016) · APL Materials · doi:10.1063/1.4946894 · Cited by 1,385 · see citing works
Accelerating materials property predictions using machine learning
Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R. (2013) · Scientific Reports · doi:10.1038/srep02810 · Cited by 851 · see citing works
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809 · see citing works
Lafon, S., & Lee, A. B. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.184 · Cited by 620 · see citing works
Artificial neural networks applied to polymer composites: a review
Zhang, Z., & FRIEDRICH, K. (2003) · Composites Science and Technology · doi:10.1016/s0266-3538(03)00106-4 · Cited by 577 · see citing works
De novo composite design based on machine learning algorithm
Gu, G. X., Chen, C., & Buehler, M. J. (2017) · Extreme Mechanics Letters · doi:10.1016/j.eml.2017.10.001 · Cited by 470 · see citing works
Self-citationBiomimetic shark skin: design, fabrication and hydrodynamic function
Wen, L., Weaver, J. C., & LAUDER, G. V. (2014) · Journal of Experimental Biology · doi:10.1242/jeb.097097 · Cited by 469 · see citing works
Hierarchically Enhanced Impact Resistance of Bioinspired Composites
Gu, G. X., Takaffoli, M., & Buehler, M. J. (2017) · Advanced Materials · doi:10.1002/adma.201700060 · Cited by 400 · see citing works
Self-citationGaynor, A. T., Meisel, N. A., Williams, C. B., & Guest, J. K. (2014) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4028439 · Cited by 271 · see citing works
Ren, X. Y., Shen, J., Ghaedizadeh, A., Tian, H.-Q., & Xie, Y. M. (2015) · Smart Materials and Structures · doi:10.1088/0964-1726/24/9/095016 · Cited by 210 · see citing works
Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. (2016) · Morgan Kaufmann Publishers Inc. eBooks · Cited by 199 · see citing works
Bone‐Inspired Materials by Design: Toughness Amplification Observed Using 3D Printing and Testing
Libonati, F., Gu, G. X., Zhao Qin, A., Vergani, L. M., & Buehler, M. J. (2016) · Advanced Engineering Materials · doi:10.1002/adem.201600143 · Cited by 175 · see citing works
Self-citationBiomimetic additive manufactured polymer composites for improved impact resistance
Gu, G. X., Takaffoli, M., Hsieh, A. J., & Buehler, M. J. (2016) · Extreme Mechanics Letters · doi:10.1016/j.eml.2016.09.006 · Cited by 174 · see citing works
Self-citationLin, E., Li, Y., Ortiz, C., & Boyce, M. C. (2014) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2014.08.011 · Cited by 170 · see citing works
Simple cubic three-dimensional auxetic metamaterials
Shen, J., Zhou, S., Huang, X., & Xie, Y. M. (2014) · physica status solidi (b) · doi:10.1002/pssb.201451304 · Cited by 150 · see citing works
Printing nature: Unraveling the role of nacre's mineral bridges
Gu, G. X., Libonati, F., Wettermark, S. D., & Buehler, M. J. (2017) · Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · doi:10.1016/j.jmbbm.2017.05.007 · Cited by 148 · see citing works
Self-citationPost-buckling optimisation of composite stiffened panels using neural networks
Bisagni, C., & Lanzi, L. (2002) · Composite Structures · doi:10.1016/s0263-8223(02)00053-3 · Cited by 145 · see citing works
Inferring low-dimensional microstructure representations using convolutional neural networks
Lubbers, N., Lookman, T., & Barros, K. M. (2017) · Physical review. E · doi:10.1103/physreve.96.052111 · Cited by 143 · see citing works
Three-Dimensional-Printing of Bio-Inspired Composites
Gu, G. X., Su, I., Sharma, S., Voros, J. L., Zhao Qin, A., & Buehler, M. J. (2016) · Journal of Biomechanical Engineering · doi:10.1115/1.4032423 · Cited by 142 · see citing works
Self-citationHierarchical Fibers with a Negative Poisson’s Ratio for Tougher Composites
Sun, Y., & Pugno, N. M. (2013) · Materials · doi:10.3390/ma6020699 · Cited by 93 · see citing works
Optimization of Composite Fracture Properties: Method, Validation, and Applications
Gu, G. X., Dimas, L. S., Zhao Qin, A., & Buehler, M. J. (2016) · Journal of Applied Mechanics · doi:10.1115/1.4033381 · Cited by 93 · see citing works
Self-citationGeographical Coarse Graining of Complex Networks
Kim, B. J. (2004) · Physical Review Letters · doi:10.1103/physrevlett.93.168701 · Cited by 63 · see citing works
Gu, G. X., Wettermark, S. D., & Buehler, M. J. (2017) · Additive manufacturing · doi:10.1016/j.addma.2017.07.002 · Cited by 58 · see citing works
Self-citationChen, C., Martín‐Martínez, F. J., Ling, S., Zhao Qin, A., & Buehler, M. J. (2017) · Nano Futures · doi:10.1088/2399-1984/aa6aed · Cited by 51 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 33
- R. O. Ritchie, Nat. Mater., 2011, 10, 817.
- P. Fratzl and R. Weinkamer, Prog. Mater. Sci., 2007, 52, 1263-1334.
- U. G. Wegst, H. Bai, E. Saiz, A. P. Tomsia and R. O. Ritchie, Nat. Mater., 2015, 14, 23-36.
- C. Sanchez, H. Arribart and M. M. G. Guille, Nat. Mater., 2005, 4, 277.
- C.-T. Chen, F. J. Martin-Martinez, S. Ling, Z. Qin and M. J. Buehler, Nano Futures, 2017, 1, 011003.
- W. Gao, Y. Zhang, D. Ramanujan, K. Ramani, Y. Chen, C. B. Williams, C. C. Wang, Y. C. Shin, S. Zhang and P. D. Zavattieri, Comput.-Aided Des., 2015, 69, 65-89.
- G. X. Gu, F. Libonati, S. Wettermark and M. J. Buehler, J. Mech. Behav. Biomed. Mater., 2017, 76, 135-144.
- F. Libonati, G. X. Gu, Z. Qin, L. Vergani and M. J. Buehler, Adv. Eng. Mater., 2016, 18, 1354-1363.
- G. X. Gu, I. Su, S. Sharma, J. L. Voros, Z. Qin and M. J. Buehler, J. Biomech. Eng., 2016, 138, 021006.
- E. Lin, Y. Li, C. Ortiz and M. C. Boyce, J. Mech. Phys. Solids, 2014, 73, 166-182.
- G. X. Gu, M. Takaffoli and M. J. Buehler, Adv. Mater., 2017, 29, 1700060.
- G. X. Gu, M. Takaffoli, A. J. Hsieh and M. J. Buehler, Extreme Mech. Lett., 2016, 9, 317-323.
- L. Wen, J. C. Weaver and G. V. Lauder, J. Exp. Biol., 2014, 217, 1656-1666.
- B. G. Compton and J. A. Lewis, Adv. Mater., 2014, 26, 5930-5935.
- X. Ren, J. Shen, A. Ghaedizadeh, H. Tian and Y. M. Xie, Smart Mater. Struct., 2015, 24, 095016.
- J. Shen, S. Zhou, X. Huang and Y. M. Xie, Phys. Status Solidi B, 2014, 251, 1515-1522.
- Y. Sun and N. Pugno, Materials, 2013, 6, 699-712.
- D. Brackett, I. Ashcroft and R. Hague, Proceedings of the solid freeform fabrication symposium, 2011, pp. 348-362.
- G. X. Gu, L. Dimas, Z. Qin and M. J. Buehler, J. Appl. Mech., 2016, 83, 071006.
- G. X. Gu, S. Wettermark and M. J. Buehler, Addit. Manuf., 2017, 17, 47-54.
- A. T. Gaynor, N. A. Meisel, C. B. Williams and J. K. Guest, J. Manuf. Sci. Eng., 2014, 136, 061015.
- A. L. Blum and P. Langley, Artif. Intell., 1997, 97, 245-271.
- M. S. Bartlett, J. R. Movellan and T. J. Sejnowski, IEEE Trans. neural networks, 2002, 13, 1450-1464.
- G. X. Gu, C.-T. Chen and M. J. Buehler, Extreme Mech. Lett., 2017, 18, 19-28.
- N. Lubbers, T. Lookman and K. Barros, Phys. Rev. E, 2017, 96, 052111.
- B. J. Kim, Phys. Rev. Lett., 2004, 93, 168701.
- S. Lafon and A. B. Lee, IEEE transactions on pattern analysis and machine intelligence, 2006, 28, 1393-1403.
- A. Agrawal and A. Choudhary, APL Mater., 2016, 4, 053208.
- G. B. Goh, N. O. Hodas and A. Vishnu, J. Comput. Chem., 2017, 38, 1291-1307.
- G. Pilania, C. Wang, X. Jiang, S. Rajasekaran and R. Ramprasad, Sci. Rep., 2013, 3, 2810.
- Z. Zhang and K. Friedrich, Compos. Sci. Technol., 2003, 63, 2029-2044.
- R. Liu, A. Kumar, Z. Chen, A. Agrawal, V. Sundararaghavan and A. Choudhary, Sci. Rep., 2015, 5, 1551.
- C. Bisagni and L. Lanzi, Compos. Struct., 2002, 58, 237-247.
Cited by
Sorted by citations
Voxelated soft matter via multimaterial multinozzle 3D printing
Skylar‐Scott, M. A., Mueller, J., Visser, C. W., & Lewis, J. A. (2019) · Nature · doi:10.1038/s41586-019-1736-8 · Cited by 1,031 · see citing works
Magnetic Soft Materials and Robots
Kim, Y., & Zhao, X. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00481 · Cited by 897 · see citing works
Benedetti, M., du Plessis, A., Ritchie, R. O., Dallago, M., Razavi, N., & Berto, F. (2021) · Materials Science and Engineering: R: Reports · doi:10.1016/j.mser.2021.100606 · Cited by 788 · see citing works
Machine learning in additive manufacturing: State-of-the-art and perspectives
Wang, C., Tan, X., Tor, S. B., & Lim, C. S. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101538 · Cited by 713 · see citing works
Artificial intelligence and machine learning in design of mechanical materials
Guo, K., Yang, Z., Yu, C., & Buehler, M. J. (2020) · Materials Horizons · doi:10.1039/d0mh01451f · Cited by 702 · see citing works
Self-citationA review on machine learning in 3D printing: applications, potential, and challenges
Goh, G. D., Sing, S. L., & Yeong, W. Y. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09876-9 · Cited by 664 · see citing works
Hierarchically structured bioinspired nanocomposites
Nepal, D., Kang, S., Adstedt, K. M., Kanhaiya, K., Bockstaller, M. R., Brinson, L. C., Buehler, M. J., Coveney, P. V., Dayal, K., El‐Awady, J. A., et al. (2022) · Nature Materials · doi:10.1038/s41563-022-01384-1 · Cited by 622 · see citing works
Self-citation4D printing: Fundamentals, materials, applications and challenges
Ahmed, A., Arya, S. K., Gupta, V., Furukawa, H., & Khosla, A. (2021) · Polymer · doi:10.1016/j.polymer.2021.123926 · Cited by 523 · see citing works
Prediction of composite microstructure stress-strain curves using convolutional neural networks
Yang, C., Kim, Y., Ryu, S., & Gu, G. X. (2020) · Materials & Design · doi:10.1016/j.matdes.2020.108509 · Cited by 456 · see citing works
Self-citationBeautiful and Functional: A Review of Biomimetic Design in Additive Manufacturing
du Plessis, A., Broeckhoven, C., Yadroitsava, I., Yadroitsev, I., Hands, C. H., Kunju, R., & Bhate, D. (2019) · Additive Manufacturing · doi:10.1016/j.addma.2019.03.033 · Cited by 448 · see citing works
Wu, Y., Fang, J., Wu, C., Li, C., Sun, G., & Li, Q. (2023) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2023.108102 · Cited by 447 · see citing works
Ha, C. S., Yao, D., Xu, Z., Liu, C., Liu, H., Elkins, D., Kile, M., Deshpande, V. S., Kong, Z., Bauchy, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40854-1 · Cited by 400 · see citing works
Developments in 4D-printing: a review on current smart materials, technologies, and applications
Zhang, Z., Demir, K. G., & Gu, G. X. (2019) · International Journal of Smart and Nano Materials · doi:10.1080/19475411.2019.1591541 · Cited by 393 · see citing works
Self-citationDeep learning model to predict complex stress and strain fields in hierarchical composites
Yang, Z., Yu, C., & Buehler, M. J. (2021) · Science Advances · doi:10.1126/sciadv.abd7416 · Cited by 391 · see citing works
Self-citationDeep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design
Zheng, X., Zhang, X., Chen, T., & Watanabe, I. (2023) · Advanced Materials · doi:10.1002/adma.202302530 · Cited by 386 · see citing works
Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M. K., Gaur, V., Królczyk, G., & Wu, C. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02029-5 · Cited by 351 · see citing works
Designing complex architectured materials with generative adversarial networks
Mao, Y., He, Q., & Zhao, X. (2020) · Science Advances · doi:10.1126/sciadv.aaz4169 · Cited by 339 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Chen, C., & Gu, G. X. (2020) · Advanced Science · doi:10.1002/advs.201902607 · Cited by 332 · see citing works
Self-citationMachine learning for composite materials
Chen, C., & Gu, G. X. (2019) · MRS Communications · doi:10.1557/mrc.2019.32 · Cited by 317 · see citing works
Self-citationMachine Learning‐Driven Biomaterials Evolution
Suwardi, A., Wang, F., Xue, K., Han, M., Teo, P., Wang, P., Wang, S., Liu, Y., Ye, E., Zibiao, L., et al. (2021) · Advanced Materials · doi:10.1002/adma.202102703 · Cited by 307 · see citing works
Sharma, A., Mukhopadhyay, T., Rangappa, S. M., Siengchin, S., & Kushvaha, V. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09700-9 · Cited by 295 · see citing works
Jiang, J., & Ma, Y. (2020) · Micromachines · doi:10.3390/mi11070633 · Cited by 283 · see citing works
Machine Learning for Advanced Additive Manufacturing
Jin, Z., Zhang, Z., Demir, K. G., & Gu, G. X. (2020) · Matter · doi:10.1016/j.matt.2020.08.023 · Cited by 275 · see citing works
Self-citationAdditive manufacturing of polymeric composites from material processing to structural design
Yuan, S., Li, S., Zhu, J., & Tang, Y. (2021) · Composites Part B Engineering · doi:10.1016/j.compositesb.2021.108903 · Cited by 272 · see citing works
Luo, L., Zhang, F., Wang, L., Liu, Y., & Leng, J. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202312036 · Cited by 271 · see citing works
Artificial Intelligence in Predicting Mechanical Properties of Composite Materials
Kibrete, F., Trzepieciński, T., Gebremedhen, H. S., & Woldemichael, D. E. (2023) · Journal of Composites Science · doi:10.3390/jcs7090364 · Cited by 266 · see citing works
Data‐Driven Design for Metamaterials and Multiscale Systems: A Review
Lee, D., Chen, W., Wang, L., Chen, W., & Chen, W. (2023) · Advanced Materials · doi:10.1002/adma.202305254 · Cited by 246 · see citing works
Applications of artificial intelligence in engineering and manufacturing: a systematic review
Nti, I. K., Adekoya, A. F., Weyori, B. A., & Nyarko‐Boateng, O. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01771-6 · Cited by 243 · see citing works
Jin, Z., Zhang, Z., & Gu, G. X. (2019) · Manufacturing Letters · doi:10.1016/j.mfglet.2019.09.005 · Cited by 241 · see citing works
Self-citationAbueidda, D., Almasri, M., Ammourah, R., Ravaioli, U., Jasiuk, I., & Sobh, N. (2019) · Composite Structures · doi:10.1016/j.compstruct.2019.111264 · Cited by 239 · see citing works
Evolution of 3D Printing Methods and Materials for Electrochemical Energy Storage
Egorov, V. M., Gulzar, U., Zhang, Y., Breen, S., & O’Dwyer, C. (2020) · Advanced Materials · doi:10.1002/adma.202000556 · Cited by 237 · see citing works
Bishara, D., Xie, Y., Liu, W. K., & Li, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09795-8 · Cited by 228 · see citing works
Topology optimization of 2D structures with nonlinearities using deep learning
Abueidda, D., Korić, S., & Sobh, N. (2020) · Computers & Structures · doi:10.1016/j.compstruc.2020.106283 · Cited by 224 · see citing works
Architectural Design and Additive Manufacturing of Mechanical Metamaterials: A Review
Lu, C., Hsieh, M.-T., Huang, Z., Zhang, C., Lin, Y., Shen, Q., Chen, F., & Zhang, L. (2022) · Engineering · doi:10.1016/j.eng.2021.12.023 · Cited by 217 · see citing works
Inspiration from Nature's body armours – A review of biological and bioinspired composites
Ghazlan, A., Ngo, T., Tan, P., Xie, Y. M., Tran, P., & Donough, M. J. (2020) · Composites Part B Engineering · doi:10.1016/j.compositesb.2020.108513 · Cited by 215 · see citing works
Maurizi, M., Gao, C., & Berto, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-26424-3 · Cited by 203 · see citing works
Machine learning in predicting mechanical behavior of additively manufactured parts
Nasiri, S., & Khosravani, M. R. (2021) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2021.07.004 · Cited by 202 · see citing works
Materials and technical innovations in 3D printing in biomedical applications
Tetsuka, H., & Shin, S. R. (2020) · Journal of Materials Chemistry B · doi:10.1039/d0tb00034e · Cited by 200 · see citing works
Programmable multi-physical mechanics of mechanical metamaterials
Sinha, P. K., & Mukhopadhyay, T. (2023) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2023.100745 · Cited by 198 · see citing works
Role of metal 3D printing to increase quality and resource-efficiency in the construction sector
Kanyilmaz, A., Demir, A. G., Chierici, M., Berto, F., Gardner, L., Kandukuri, S. Y., Kassabian, P. E., Kinoshita, T., Laurenti, A., Paoletti, I., et al. (2021) · Additive manufacturing · doi:10.1016/j.addma.2021.102541 · Cited by 195 · see citing works
Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
Liu, Z., & Wu, C. (2019) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2019.03.004 · Cited by 194 · see citing works
Recent Advances in 4D Printing of Advanced Materials and Structures for Functional Applications
Wan, X., Xiao, Z., Tian, Y., Chen, M., Liu, F., Wang, D., Liu, Y., Bártolo, P., Yan, C., Shi, Y., et al. (2024) · Advanced Materials · doi:10.1002/adma.202312263 · Cited by 194 · see citing works
Deng, B., Zareei, A., Ding, X., Weaver, J. C., Rycroft, C. H., & Bertoldi, K. (2022) · Advanced Materials · doi:10.1002/adma.202206238 · Cited by 189 · see citing works
Unleashing the Power of Artificial Intelligence in Materials Design
Badini, S., Regondi, S., & Pugliese, R. (2023) · Materials · doi:10.3390/ma16175927 · Cited by 187 · see citing works
Biological Material Interfaces as Inspiration for Mechanical and Optical Material Designs
Ren, J., Wang, Y., Yao, Y., Wang, Y., Fei, X., Qi, P., Lin, S., Kaplan, D. L., Buehler, M. J., & Ling, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00416 · Cited by 184 · see citing works
Self-citationHarnessing artificial intelligence for the next generation of 3D printed medicines
Elbadawi, M., McCoubrey, L. E., Gavins, F. K. H., Ong, J. J., Goyanes, Á., Gaisford, S., & Basit, A. W. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.05.015 · Cited by 178 · see citing works
Bhate, D., Penick, C. A., Ferry, L. A., & Lee, C. (2019) · Designs · doi:10.3390/designs3010019 · Cited by 177 · see citing works
Kim, Y., Kim, Y., Yang, C., Park, K., Gu, G. X., & Ryu, S. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00609-2 · Cited by 177 · see citing works
Self-citationJin, Z., Zhang, Z., & Gu, G. X. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900130 · Cited by 176 · see citing works
Self-citationUsing Deep Learning to Predict Fracture Patterns in Crystalline Solids
Hsu, Y.-C., Yu, C., & Buehler, M. J. (2020) · Matter · doi:10.1016/j.matt.2020.04.019 · Cited by 174 · see citing works
Self-citationAccelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning
Hanakata, P. Z., Cubuk, E. D., Campbell, D. K., & Park, H. S. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.255304 · Cited by 171 · see citing works
The trends and challenges of fiber reinforced additive manufacturing
Fidan, I., Imeri, A., Gupta, A., Hasanov, S., Nasirov, A., Elliott, A., Alifui‐Segbaya, F., & Nanami, N. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-03269-7 · Cited by 170 · see citing works
Engineering lattice metamaterials for extreme property, programmability, and multifunctionality
Jia, Z., Liu, F., Jiang, X., & Wang, L. (2020) · Journal of Applied Physics · doi:10.1063/5.0004724 · Cited by 169 · see citing works
Li, X., Yu, X., & Zhai, W. (2021) · Advanced Materials · doi:10.1002/adma.202104552 · Cited by 167 · see citing works
Artificial Neural Network Algorithms for 3D Printing
Mahmood, M. A., Vișan, A. I., Ristoscu, C., & Mihailescu, I. N. (2020) · Materials · doi:10.3390/ma14010163 · Cited by 165 · see citing works
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Jin, H., Zhang, E., & Espinosa, H. D. (2023) · Applied Mechanics Reviews · doi:10.1115/1.4062966 · Cited by 163 · see citing works
Yue, L., Montgomery, S. M., Sun, X., Yu, L., Song, Y., Nomura, T., Tanaka, M., & Qi, H. J. (2023) · Nature Communications · doi:10.1038/s41467-023-36909-y · Cited by 157 · see citing works
Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures
Sun, X., Yue, L., Yu, L., Shao, H., Peng, X., Zhou, K., Demoly, F., Zhao, R. R., & Qi, H. J. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202109805 · Cited by 145 · see citing works
Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends
Jiao, P., & Alavi, A. H. (2020) · International Materials Reviews · doi:10.1080/09506608.2020.1815394 · Cited by 144 · see citing works
Liu, F., Jiang, X., Wang, X., & Wang, L. (2020) · Extreme Mechanics Letters · doi:10.1016/j.eml.2020.101002 · Cited by 142 · see citing works
Barthwal, S., Uniyal, S., & Barthwal, S. (2024) · Micromachines · doi:10.3390/mi15030391 · Cited by 140 · see citing works
Additive manufacturing in drug delivery applications: A review
Reddy, R. D. P., & Sharma, V. (2020) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2020.119820 · Cited by 135 · see citing works
Accelerating Auxetic Metamaterial Design with Deep Learning
Wilt, J. K., Yang, C., & Gu, G. X. (2019) · Advanced Engineering Materials · doi:10.1002/adem.201901266 · Cited by 134 · see citing works
Self-citationMachine learning for polymer composites process simulation – a review
Cassola, S., Duhovic, M., Schmidt, T., & May, D. (2022) · Composites Part B Engineering · doi:10.1016/j.compositesb.2022.110208 · Cited by 134 · see citing works
Machine Learning-Guided Three-Dimensional Printing of Tissue Engineering Scaffolds
Conev, A., Litsa, E. E., Perez, M. R., Diba, M., Mikos, A. G., & Kavraki, L. E. (2020) · Tissue Engineering Part A · doi:10.1089/ten.tea.2020.0191 · Cited by 126 · see citing works
Artificial Intelligence in Biomaterials: A Comprehensive Review
Gokcekuyu, Y., Ekinci, F., Güzel, M. S., Açıcı, K., Aydin, S., & Aşuroğlu, T. (2024) · Applied Sciences · doi:10.3390/app14156590 · Cited by 126 · see citing works
Disrupting 3D printing of medicines with machine learning
Elbadawi, M., McCoubrey, L. E., Gavins, F. K. H., Ong, J. J., Goyanes, Á., Gaisford, S., & Basit, A. W. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2021.06.002 · Cited by 125 · see citing works
Yu, C., Zhao Qin, A., Martín‐Martínez, F. J., & Buehler, M. J. (2019) · ACS Nano · doi:10.1021/acsnano.9b02180 · Cited by 123 · see citing works
Self-citationPragmatic generative optimization of novel structural lattice metamaterials with machine learning
Garland, A. P., White, B. C., Jensen, S., & Boyce, B. L. (2021) · Materials & Design · doi:10.1016/j.matdes.2021.109632 · Cited by 122 · see citing works
Bagherzadeh, F., Shafighfard, T., Khan, R. M. A., Szczuko, P., & Mieloszyk, M. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110315 · Cited by 122 · see citing works
Recent advances in additive manufacturing of active mechanical metamaterials
Montgomery, S. M., Kuang, X., Armstrong, C. D., & Qi, H. J. (2020) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2020.100869 · Cited by 120 · see citing works
Machine learning and deep learning in phononic crystals and metamaterials – A review
Muhammad, Kennedy, J. F., & Lim, C. W. (2022) · Materials Today Communications · doi:10.1016/j.mtcomm.2022.104606 · Cited by 119 · see citing works
Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118 · see citing works
Okafor, C. E., Iweriolor, S., Ani, O. I., Ahmad, S., Mehfuz, S., Ekwueme, G. O., Chukwumuanya, O. E., Abonyi, S. E., Ekengwu, I. E., & Chikelu, P. O. (2023) · Hybrid Advances · doi:10.1016/j.hybadv.2023.100026 · Cited by 118 · see citing works
Architected metamaterials with tailored 3D buckling mechanisms at the microscale
Vangelatos, Z., Gu, G. X., & Grigoropoulos, C. P. (2019) · Extreme Mechanics Letters · doi:10.1016/j.eml.2019.100580 · Cited by 117 · see citing works
Self-citationChampa-Bujaico, E., García-Díaz, P., & Diez-Pascual, A. M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231810712 · Cited by 117 · see citing works
Impact-resistant materials inspired by the mantis shrimp's dactyl club
Behera, R. P., & Ferrand, H. L. (2021) · Matter · doi:10.1016/j.matt.2021.07.012 · Cited by 115 · see citing works
A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification
Thobiani, F. A., Khatir, S., Benaissa, B., Ghandourah, E. I., Mirjalili, S., & Wahab, M. M. A. (2021) · Theoretical and Applied Fracture Mechanics · doi:10.1016/j.tafmec.2021.103213 · Cited by 114 · see citing works
Inverse design of truss lattice materials with superior buckling resistance
Maurizi, M., Gao, C., & Berto, F. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00938-w · Cited by 111 · see citing works
Machine learning-enabled forward prediction and inverse design of 4D-printed active plates
Sun, X., Yue, L., Yu, L., Forte, C. T., Armstrong, C. D., Zhou, K., Demoly, F., Zhao, R. R., & Qi, H. J. (2024) · Nature Communications · doi:10.1038/s41467-024-49775-z · Cited by 111 · see citing works
Application of artificial intelligence in 3D printing physical organ models
Ma, L., Yu, S., Xu, X., Amadi, S. M., Zhang, J., & Wang, Z. (2023) · Materials Today Bio · doi:10.1016/j.mtbio.2023.100792 · Cited by 110 · see citing works
Machine intelligence in metamaterials design: a review
Cerniauskas, G., Sadia, H., & Alam, P. (2024) · Oxford Open Materials Science · doi:10.1093/oxfmat/itae001 · Cited by 109 · see citing works
Deep Reinforcement Learning for Digital Materials Design
Sui, F., Guo, R., Zhang, Z., Gu, G. X., & Lin, L. (2021) · ACS Materials Letters · doi:10.1021/acsmaterialslett.1c00390 · Cited by 108 · see citing works
Self-citationIntraoperative Bioprinting: Repairing Tissues and Organs in a Surgical Setting
Wu, Y. B., Ravnic, D. J., & Ozbolat, I. T. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.01.004 · Cited by 107 · see citing works
Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research
Kerner, J., Dogan, A. B., & von Recum, H. A. (2020) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.05.053 · Cited by 106 · see citing works
Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistance
Yu, C., Zhao Qin, A., & Buehler, M. J. (2019) · Nano Futures · doi:10.1088/2399-1984/ab36f0 · Cited by 105 · see citing works
Self-citationWu, L., Guo, T., & Li, T. (2021) · iScience · doi:10.1016/j.isci.2021.102398 · Cited by 105 · see citing works
Hassanin, H. S., Alkendi, Y., El-Sayed, M. A., Essa, K., & Zweiri, Y. (2020) · Advanced Engineering Materials · doi:10.1002/adem.201901338 · Cited by 104 · see citing works
Accelerating Auxetic Metamaterial Design with Deep Learning
Wilt, J. K., Yang, C., & Gu, G. X. (2020) · Advanced Engineering Materials · doi:10.1002/adem.202070018 · Cited by 103 · see citing works
Self-citationUsing convolutional neural networks to predict composite properties beyond the elastic limit
Yang, C., Kim, Y., Ryu, S., & Gu, G. X. (2019) · MRS Communications · doi:10.1557/mrc.2019.49 · Cited by 102 · see citing works
Self-citationConvolutional Neural Network applications in additive manufacturing: A review
Valizadeh, M., & Wolff, S. J. (2022) · Advances in Industrial and Manufacturing Engineering · doi:10.1016/j.aime.2022.100072 · Cited by 102 · see citing works
Konstantopoulos, G., Koumoulos, E. P., & Charitidis, C. A. (2022) · Nanomaterials · doi:10.3390/nano12152646 · Cited by 100 · see citing works
Nature-inspired architected materials using unsupervised deep learning
Shen, S. C.-Y., & Buehler, M. J. (2022) · Communications Engineering · doi:10.1038/s44172-022-00037-0 · Cited by 100 · see citing works
Self-citationQuantum optimization with a novel Gibbs objective function and ansatz architecture search
Li, L., Fan, M., Coram, M., Riley, P., & Leichenauer, S. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023074 · Cited by 98 · see citing works
Wu, Z., Pan, H., Huang, P., Tang, J., & She, W. (2024) · Advanced Materials · doi:10.1002/adma.202405183 · Cited by 98 · see citing works
Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization
Li, M., Dai, L., & Hu, Y. (2022) · ACS Energy Letters · doi:10.1021/acsenergylett.2c01836 · Cited by 94 · see citing works
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm
Xie, C., Qiu, H., Liu, L., You, Y., Li, H., Li, Y., Sun, Z., Lin, J., & An, L. (2025) · SmartMat · doi:10.1002/smm2.1320 · Cited by 94 · see citing works
Williams, G., Meisel, N. A., Simpson, T. W., & McComb, C. (2019) · Journal of Mechanical Design · doi:10.1115/1.4044199 · Cited by 92 · see citing works
A Bayesian regularization-backpropagation neural network model for peeling computations
Gouravaraju, S., Narayan, J., Sauer, R. A., & Gautam, S. S. (2021) · The Journal of Adhesion · doi:10.1080/00218464.2021.2001335 · Cited by 90 · see citing works
- Authors
- Gu, G. X., Chen, C.-T., Richmond, D. J., & Buehler, M. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Buehler, Markus J.
- Venue
- Materials Horizons
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2018
- Volume
- 5
- Issue
- 5
- Pages
- 939-945
- ISSN
- 2051-6347 (ISSN-L)
- 2051-6355
- OpenAlex
- W2883482411
- Cited by
- 556
- References
- 35
- Citations by year
- 2026: 53 · 2025: 72 · 2024: 85 · 2023: 85 · 2022: 87 · 2021: 71 · 2020: 71 · 2019: 30 · 2018: 2
- FWCI
- 16.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Materials Horizons (publishedVersion, cc-by)
- The Royal Society of Chemistry
- Funders
- Office of Naval Research (N000141612333)
- Air Force Office of Scientific Research (FATE MURI FA9550-15-1-0514)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Physical Sciences › Engineering › Mechanical Engineering › Cellular and Composite Structures
- Keywords
- biomimetic design, convolutional neural networks, machine learning for materials design, additive manufacturing, mechanical testing of composites
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review