The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
In the citing paper
“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.
Citations
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 25,543
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
The conflicts between strength and toughness
Ritchie, R. O. (2011) · Nature Materials · doi:10.1038/nmat3115 · Cited by 3,989
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
Nature’s hierarchical materials
Fratzl, P., & Weinkamer, R. (2007) · Progress in Materials Science · doi:10.1016/j.pmatsci.2007.06.001 · Cited by 2,788
The status, challenges, and future of additive manufacturing in engineering
Gao, W., Zhang, Y., Ramanujan, D., Ramani, K., 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 2,657
(2013) · Springer eBooks · Cited by 2,625
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 1,951
Bendsøe, M. P., & Sigmund, O. (2004) · Springer eBooks · doi:10.1007/978-3-662-05086-6 · Cited by 1,922
3D‐Printing of Lightweight Cellular Composites
Compton, B. G., & Lewis, J. A. (2014) · Advanced Materials · doi:10.1002/adma.201401804 · Cited by 1,677
Flagged first, then by citations
Research works printed but not linked: 2
- D. Brackett , I.Ashcroft and R.Hague , Proceedings of the solid freeform fabrication symposium , 2011 , pp. 348–362
- Liu, 2015, Sci. Rep., 5, 1551
Cited by
Sorted by citations
Items 101–200 of 557
Ma, C., Zhang, Z., Luce, B., Pusateri, S., Xie, B., Rafiei, M. H., & Hu, N. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0309-6 · Cited by 91
Chinchanikar, S. S., & Shaikh, A. (2022) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-022-07125-4 · Cited by 95
Deep learning model to predict fracture mechanisms of graphene
Lew, A. J., Yu, C., Hsu, Y.-C., & Buehler, M. J. (2021) · npj 2D Materials and Applications · doi:10.1038/s41699-021-00228-x · Cited by 87
Self-citationGharibshahian, M., Torkashvand, M., Bavisi, M., Aldaghi, N., & Alizadeh, A. (2024) · Skin Research and Technology · doi:10.1111/srt.70016 · Cited by 93
Coulombic Friction in Metamaterials to Dissipate Mechanical Energy
Garland, A. P., Adstedt, K. M., Casias, Z. J., White, B. C., Mook, W. M., Kaehr, B., Jared, B. H., Lester, B., Leathe, N., Schwaller, E., et al. (2020) · Extreme Mechanics Letters · doi:10.1016/j.eml.2020.100847 · Cited by 85
Learning hidden elasticity with deep neural networks
Chen, C., & Gu, G. X. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102721118 · Cited by 85
Self-citationMachine learning for accelerating the design process of double-double composite structures
Zhang, Z., Zhang, Z., Caprio, F. D., & Gu, G. X. (2022) · Composite Structures · doi:10.1016/j.compstruct.2022.115233 · Cited by 85
Self-citationMachine Learning-Driven Multidomain Nanomaterial Design: From Bibliometric Analysis to Applications
Wang, H., Cao, H., & Yang, L. (2024) · ACS Applied Nano Materials · doi:10.1021/acsanm.4c04940 · Cited by 91
Zhai, C., Li, T., Shi, H., & Yeo, J. (2020) · Journal of Materials Chemistry B · doi:10.1039/d0tb00896f · Cited by 86
Shin, D., Cupertino, A., de Jong, M. H. J., Steeneken, P. G., Bessa, M. A., & Norte, R. A. (2021) · Advanced Materials · doi:10.1002/adma.202106248 · Cited by 91
Enhanced physics‐informed neural networks for hyperelasticity
Abueidda, D., Korić, S., Guleryuz, E., & Sobh, N. (2022) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.7176 · Cited by 88
Agarwal, M., Pasupathy, P., Wu, X., Recchia, S. S., & Pelegri, A. A. (2024) · Small Science · doi:10.1002/smsc.202300185 · Cited by 83
Accurate Estimation of Tensile Strength of 3D Printed Parts Using Machine Learning Algorithms
Jayasudha, M., Muniyandy, E., Mahdal, M., & Priyadarshini, J. (2022) · Processes · doi:10.3390/pr10061158 · Cited by 81
Bioinspired interpenetrating-phase metal composites
Liu, Y., Chen, B., Liu, Z., Zhang, Z., & Ritchie, R. O. (2024) · Progress in Materials Science · doi:10.1016/j.pmatsci.2024.101281 · Cited by 81
Zhang, Y., Safdar, M., Xie, J., Li, J., Sage, M., & Zhao, Y. F. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02017-9 · Cited by 84
Shen, S. C.-Y., Khare, E., Lee, N., Saad, M. K., Kaplan, D. L., & Buehler, M. J. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00479 · Cited by 81
Self-citationStructural Analysis of Nanoscale Network Materials Using Graph Theory
Vecchio, D. A., Mahler, S. H., Hammig, M. D., & Kotov, N. A. (2021) · ACS Nano · doi:10.1021/acsnano.1c04711 · Cited by 77
Pahlavani, H., Amani, M., Saldívar, M. C., Zhou, J., Mirzaali, M. J., & ZADPOOR, A. A. (2022) · Communications Materials · doi:10.1038/s43246-022-00270-2 · Cited by 77
Emerging Trends in Bioinspired Superhydrophobic and Superoleophobic Sustainable Surfaces
Cormican, C. M., Bektaş, S., Martín‐Martínez, F. J., & Alexander, S. (2025) · Advanced Materials · doi:10.1002/adma.202415961 · Cited by 75
Digital Transformation in Materials Science: A Paradigm Change in Material's Development
Kimmig, J., Zechel, S., & Schubert, U. S. (2021) · Advanced Materials · doi:10.1002/adma.202004940 · Cited by 77
Advances in machining of hard tissues – From material removal mechanisms to tooling solutions
Zhang, Y., Robles-Linares, J. A., Chen, L., Liao, Z., Shih, A. J., & Wang, C. (2021) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2021.103838 · Cited by 73
Qiu, C., Han, Y., Shanmugam, L., Zhao, Y., Dong, S., Du, S., & Yang, J. (2021) · Composites Science and Technology · doi:10.1016/j.compscitech.2021.109154 · Cited by 73
A comprehensive review on fillers and mechanical properties of 3D printed polymer composites
Arora, N., Dua, S., Singh, V. K., Singh, S. K., & Senthilkumar, T. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.109617 · Cited by 74
A semi-supervised approach to architected materials design using graph neural networks
Guo, K., & Buehler, M. J. (2020) · Extreme Mechanics Letters · doi:10.1016/j.eml.2020.101029 · Cited by 73
Self-citationJin, Z., Zhang, Z., Ott, J., & Gu, G. X. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101696 · Cited by 70
Self-citationLew, A. J., & Buehler, M. J. (2023) · Materials Today · doi:10.1016/j.mattod.2023.03.007 · Cited by 70
Self-citationLi, K., Ma, R., Yu, Q., Gong, N., Wu, J., Wen, P., Tan, S., Zhang, Z., Murr, L. E., & Luo, J. T. (2023) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2023.118032 · Cited by 70
Chen, C., & Gu, G. X. (2019) · Advanced Theory and Simulations · doi:10.1002/adts.201900056 · Cited by 69
Self-citationWu, C., Xu, Y., Fang, J., & Li, Q. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10100-y · Cited by 71
Designing an Adhesive Pillar Shape with Deep Learning-Based Optimization
Kim, Y., Yang, C., Kim, Y., Gu, G. X., & Ryu, S. (2020) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.0c04123 · Cited by 68
Self-citationComputational discovery of microstructured composites with optimal stiffness-toughness trade-offs
Li, B., Deng, B., Shou, W., Oh, T.-H., Hu, Y., Luo, Y., Shi, L., & Matusik, W. (2024) · Science Advances · doi:10.1126/sciadv.adk4284 · Cited by 69
Hu, E., Seetoh, I. P., & Lai, C. Q. (2022) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2022.107190 · Cited by 66
Embedded 3D Printing of Multimaterial Polymer Lattices via Graph‐Based Print Path Planning
Weeks, R. D., Truby, R. L., Uzel, S. G. M., & Lewis, J. A. (2022) · Advanced Materials · doi:10.1002/adma.202206958 · Cited by 66
Machine learning assisted intelligent design of meta structures: a review
He, L., Li, Y., Torrent, D., Zhuang, X., Rabczuk, T., & Jin, Y. (2023) · Microstructures · doi:10.20517/microstructures.2023.29 · Cited by 65
Liu, F., Li, T., Jiang, X., Jia, Z., Xu, Z., & Wang, L. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101502 · Cited by 65
Nanoengineering in biomedicine: Current development and future perspectives
Jian, W., Hui, D., & Lau, D. (2020) · Nanotechnology Reviews · doi:10.1515/ntrev-2020-0053 · Cited by 66
Xu, Y., Weng, H., Ju, X., Ruan, H., Chen, J., Nan, C., Guo, J. M., & Liang, L. (2021) · Composite Structures · doi:10.1016/j.compstruct.2021.114444 · Cited by 64
Prediction of melt pool shape in additive manufacturing based on machine learning methods
Zhu, X., Jiang, F., Guo, C., Huabing, G., Wang, Z., Dong, T., & Li, H. (2022) · Optics & Laser Technology · doi:10.1016/j.optlastec.2022.108964 · Cited by 64
Lew, A. J., Jin, K., & Buehler, M. J. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01036-1 · Cited by 65
Self-citationA Review of Performance Prediction Based on Machine Learning in Materials Science
Fu, Z., Liu, W., Huang, C. S., & Mei, T. (2022) · Nanomaterials · doi:10.3390/nano12172957 · Cited by 65
Physics‐Informed Deep‐Learning For Elasticity: Forward, Inverse, and Mixed Problems
Chen, C., & Gu, G. X. (2023) · Advanced Science · doi:10.1002/advs.202300439 · Cited by 63
Self-citationComprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction
Chai, B. X., Wang, J., Dang, T. K. M., Nikzad, M., Eisenbart, B., & Fox, B. L. (2024) · Journal of Composites Science · doi:10.3390/jcs8040153 · Cited by 63
Tuning the graphene mechanical anisotropy via defect engineering
Zheng, B., & Gu, G. X. (2019) · Carbon · doi:10.1016/j.carbon.2019.09.008 · Cited by 63
Self-citationTaborda, L. L. L., Maury, H., & Pacheco, J. (2021) · Rapid Prototyping Journal · doi:10.1108/rpj-11-2019-0296 · Cited by 63
Inverse Design of Inflatable Soft Membranes Through Machine Learning
Forte, A. E., Hanakata, P. Z., Jin, L., Zari, E., Zareei, A., Fernandes, M. C., Sumner, L., Alvarez, J. T., & Bertoldi, K. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202111610 · Cited by 65
Machine learning in polymer additive manufacturing: a review
Nikooharf, M. H., Shirinbayan, M., Arabkoohi, M., Bahlouli, N., Fitoussi, J., & Benfriha, K. (2024) · International Journal of Material Forming · doi:10.1007/s12289-024-01854-8 · Cited by 62
Recent Progress of Artificial Intelligence Application in Polymer Materials
Long, T., Pang, Q., Deng, Y., Pang, X., Zhang, Y., Yang, R., & Zhou, C. (2025) · Polymers · doi:10.3390/polym17121667 · Cited by 64
Wu, W., Daneker, M., Jolley, M. A., Turner, K. T., & Lu, L. (2023) · Applied Mathematics and Mechanics · doi:10.1007/s10483-023-2995-8 · Cited by 67
Perspective: Machine Learning in Design for 3D/4D Printing
Sun, X., Zhou, K., Demoly, F., Zhao, R. R., & Qi, H. J. (2023) · Journal of Applied Mechanics · doi:10.1115/1.4063684 · Cited by 63
Desai, P. S., & Higgs, C. F. (2019) · Metals · doi:10.3390/met9111176 · Cited by 61
Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials
Zhang, Z., & Gu, G. X. (2020) · Advanced Theory and Simulations · doi:10.1002/adts.202000031 · Cited by 60
Self-citationAdvanced processing of 3D printed biocomposite materials using artificial intelligence
Verma, D., Dong, Y., Sharma, M., & Chaudhary, A. K. (2021) · Materials and Manufacturing Processes · doi:10.1080/10426914.2021.1945090 · Cited by 61
Alavi, S. E., Gholami, M., Shahmabadi, H. E., & Reher, P. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12226962 · Cited by 57
Laycock, B. G., Chan, C. M., & Halley, P. J. (2024) · Progress in Polymer Science · doi:10.1016/j.progpolymsci.2024.101874 · Cited by 59
Biomaterials by design: Harnessing data for future development
Xue, K., Wang, F., Suwardi, A., Han, M., Teo, P., Wang, P., Wang, S., Ye, E., Li, Z., & Loh, X. J. (2021) · Materials Today Bio · doi:10.1016/j.mtbio.2021.100165 · Cited by 60
4D Printing of Electroactive Materials
Chen, A. Y., Pegg, E., Chen, A., Jin, Z., & Gu, G. X. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100019 · Cited by 58
Self-citationNiu, S., & Srivastava, V. (2022) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2022.111521 · Cited by 57
Taylor, J. M., Luan, H., Lewis, J. A., Rogers, J. A., Nuzzo, R. G., & Braun, P. V. (2022) · Advanced Materials · doi:10.1002/adma.202108391 · Cited by 59
Preethikaharshini, J., Naresh, K., Rajeshkumar, G., Arumugaprabu, V., Khan, M., & Khan, K. A. (2022) · Journal of Materials Science · doi:10.1007/s10853-022-07558-1 · Cited by 58
Zhang, W., Yu, T., & Xu, J. (2022) · Extreme Mechanics Letters · doi:10.1016/j.eml.2022.101640 · Cited by 54
Deep learning approach to assess damage mechanics of bone tissue
Shen, S. C.-Y., Fernández, M. P., Tozzi, G., & Buehler, M. J. (2021) · Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · doi:10.1016/j.jmbbm.2021.104761 · Cited by 58
Self-citationMachine Learning for Additive Manufacturing
Grierson, D., Rennie, A., & Quayle, S. (2021) · Encyclopedia · doi:10.3390/encyclopedia1030048 · Cited by 54
Research status and prospect of machine learning in construction 3D printing
Geng, S., Luo, Q., Liu, K., Li, Y., Hou, Y., & Long, W.-J. (2023) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2023.e01952 · Cited by 56
Li, Z., Ma, W., Zhu, H., Deng, G., Hou, L., Xu, P., & Yao, S. (2021) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2021.1958032 · Cited by 52
Buehler, M. J. (2022) · Materials Today · doi:10.1016/j.mattod.2022.05.020 · Cited by 55
Self-citationVyavahare, S. S., Teraiya, S., & Kumar, S. (2023) · International Journal of Solids and Structures · doi:10.1016/j.ijsolstr.2023.112126 · Cited by 52
Learning finite element convergence with the Multi-fidelity Graph Neural Network
Black, N., & Najafi, A. R. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.115120 · Cited by 51
Lu, W., Kaplan, D. L., & Buehler, M. J. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202311324 · Cited by 53
Self-citationMechanical behavior of nanocomposites
Buehler, M. J., & Misra, A. (2019) · MRS Bulletin · doi:10.1557/mrs.2018.323 · Cited by 51
Self-citationCharacterising soft matter using machine learning
Clegg, P. S. (2021) · Soft Matter · doi:10.1039/d0sm01686a · Cited by 52
Freed, Y., Salviato, M., & Zobeiry, N. (2022) · International Journal of Adhesion and Adhesives · doi:10.1016/j.ijadhadh.2022.103226 · Cited by 50
Artificial intelligence for life sciences: A comprehensive guide and future trends
Luo, M., Yang, W., Bai, L., Zhang, L., Huang, J., Cao, Y.-H., Xie, Y., Tong, L., Zhang, H., Lei, Y., et al. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100105 · Cited by 51
Yu, C., & Buehler, M. J. (2020) · APL Bioengineering · doi:10.1063/1.5133026 · Cited by 50
Self-citationJung, J., Kim, Y., Park, J., & Ryu, S. (2022) · Composite Structures · doi:10.1016/j.compstruct.2022.115210 · Cited by 50
Sun, X., Yu, L., Yue, L., Zhou, K., Demoly, F., Zhao, R. R., & Qi, H. J. (2024) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2024.105561 · Cited by 50
Nano-topology optimization for materials design with atom-by-atom control
Chen, C., Chrzan, D. C., & Gu, G. X. (2020) · Nature Communications · doi:10.1038/s41467-020-17570-1 · Cited by 47
Self-citationMachine-learning micropattern manufacturing
Wang, S., Shen, Z., Shen, Z., Dong, Y., Li, Y., Cao, Y., Zhang, Y., Guo, S., Shuai, J., Yang, Y. M., et al. (2021) · Nano Today · doi:10.1016/j.nantod.2021.101152 · Cited by 47
Fatehi, E., Sarvestani, H. Y., Ashrafi, B., & Akbarzadeh, A. H. (2021) · Materials & Design · doi:10.1016/j.matdes.2021.110056 · Cited by 48
Sarkon, G. K., Safaei, B., Kenevisi, M. S., Arman, S., & Zeeshan, Q. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09786-9 · Cited by 47
Artificial Intelligence in the Design of Innovative Metamaterials: A Comprehensive Review
Song, J., Lee, J., Kim, N., & Min, K. (2023) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-023-00857-w · Cited by 45
Machine learning for expediting next-generation of fire-retardant polymer composites
Jafari, P., Zhang, R., Huo, S., Wang, Q., Yong, J., Hong, M., Deo, R. C., Wang, H., & Song, P. (2023) · Composites Communications · doi:10.1016/j.coco.2023.101806 · Cited by 50
Quantitative biomimetics of high-performance materials
Yang, M., & Kotov, N. A. (2024) · Nature Reviews Materials · doi:10.1038/s41578-024-00753-3 · Cited by 46
Molecular generation targeting desired electronic properties via deep generative models
Yuan, Q., Santana‐Bonilla, A., Zwijnenburg, M. A., & Jelfs, K. E. (2020) · Nanoscale · doi:10.1039/c9nr10687a · Cited by 47
Xu, Y., Gao, Y., Wu, C., Fang, J., Sun, G., Steven, G. P., & Li, Q. (2021) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.6809 · Cited by 44
Lew, A. J., & Buehler, M. J. (2021) · Applied Physics Reviews · doi:10.1063/5.0057162 · Cited by 45
Self-citationMilazzo, M., & Libonati, F. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202100278 · Cited by 43
Inverse-designed growth-based cellular metamaterials
Sant, S. V. ’., Thakolkaran, P., Martínez, J., & Kumar, S. (2023) · Mechanics of Materials · doi:10.1016/j.mechmat.2023.104668 · Cited by 45
Peloquin, J., Kirillova, A., Rudin, C. D., Brinson, L. C., & Gall, K. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.112126 · Cited by 43
Deshmankar, A., Challa, J. S., Singh, A. K., & Regalla, S. P. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4066575 · Cited by 44
Incorporation of machine learning in additive manufacturing: a review
Raza, A., Deen, K. M., Jaafreh, R., Hamad, K., Haider, A., & Haider, W. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09916-4 · Cited by 46
Buckling of laminated composite skew plate using FEM and machine learning methods
Mishra, B. B., Kumar, A., Samui, P., & Roshni, T. (2020) · Engineering Computations · doi:10.1108/ec-08-2019-0346 · Cited by 42
Nguyen, H. T., Nguyen, K. T. Q., Le, T. C., & Zhang, G. (2021) · Molecules · doi:10.3390/molecules26041022 · Cited by 43
Cephalo: Multi‐Modal Vision‐Language Models for Bio‐Inspired Materials Analysis and Design
Buehler, M. J. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202409531 · Cited by 43
Self-citationAzher, K., Nazir, A., Farooq, M. U., Haq, M. R. U., Ali, Z., Dalaq, A. S., Abubakar, A. A., Hussain, S., Syed, M. N., Ullah, A., et al. (2025) · Advanced Materials Technologies · doi:10.1002/admt.202401369 · Cited by 41
Shah, V., Zadourian, S., Yang, C., Zhang, Z., & Gu, G. X. (2022) · Materials Advances · doi:10.1039/d2ma00698g · Cited by 40
Self-citationMalakar, P., Thakur, M. S. H., Nahid, S. M., & Islam, M. M. (2022) · ACS Applied Nano Materials · doi:10.1021/acsanm.2c03564 · Cited by 39
A finite-element-informed neural network for parametric simulation in structural mechanics
Le-Duc, T., Nguyen‐Xuan, H., & Lee, J. (2023) · Finite Elements in Analysis and Design · doi:10.1016/j.finel.2022.103904 · Cited by 39
Zhang, S., He, X., Xia, X., Xiao, P., Wu, Q., Zheng, F., & Lu, Q. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c05376 · Cited by 39
Song, S., & Jin, H. (2024) · Soft Matter · doi:10.1039/d4sm00001c · Cited by 39
Machine learning in additive manufacturing: A comprehensive insight
Equbal, M. A., Equbal, A., Khan, Z. A., & Badruddin, I. A. (2024) · International Journal of Lightweight Materials and Manufacture · doi:10.1016/j.ijlmm.2024.10.002 · Cited by 39
Metadata
- 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 › Industrial and Manufacturing 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