Page 81 - IJAMD-1-2

International Journal of AI for
Materials and Design
Machine learning for gel fraction prediction

crosstalk‐free, fast‐responding, wide‐sensing‐range tactile doi: 10.1007/s10845-023-02167-4
fingertip sensor for smart gloves. Adv Mater Interfaces. 45. Shi J, Song J, Song B, Lu WF. Multi-objective optimization
2022;9(21):2200621.
design through machine learning for drop-on-demand
doi: 10.1002/admi.202200621 bioprinting. Engineering. 2019;5(3):586-593.
38. Liang F, Valdes JP, Cheng S, et al. Liquid-liquid dispersion doi: 10.1016/j.eng.2018.12.009
performance prediction and uncertainty quantification
using recurrent neural networks. Ind Eng Chem Res. 46. Ning H, Zhou T, Joo SW. Machine learning boosts three-
2024;63(17):7853-7875. dimensional bioprinting. Int J Bioprint. 2023;9(4):739.
doi: 10.1021/acs.iecr.4c00014 doi: 10.18063/ijb.739
39. Cheng S, Quilodrán-Casas C, Ouala S, et al. Machine learning 47. Loessner D, Meinert C, Kaemmerer E, et al. Functionalization,
with data assimilation and uncertainty quantification for preparation and use of cell-laden gelatin methacryloyl-
dynamical systems: A review. IEEE/CAA J Autom Sin. based hydrogels as modular tissue culture platforms. Nat
2023;10(6):1361-1387. Protoc. 2016;11(4):727-746.
doi: 10.48550/arXiv.2303.10462 doi: 10.1038/nprot.2016.037
40. Nathanael K, Cheng S, Kovalchuk NM, Arcucci R, 48. Ghosh RN, Thomas J, Vaidehi BR, et al. An insight
Simmons MJ. Optimization of microfluidic synthesis of into synthesis, properties and applications of gelatin
silver nanoparticles: A generic approach using machine methacryloyl hydrogel for 3D bioprinting. Mater Adv.
learning. Chem Eng Res Des. 2023;193:65-74. 2023;4(22):5496-5529.
doi: 10.1016/j.cherd.2023.03.007 doi: 10.1039/D3MA00715D
41. Xia Z, Ma K, Cheng S, et al. Accurate identification and 49. Hogan NJ, Urban AS, Ayala-Orozco C, Pimpinelli A,
measurement of the precipitate area by two-stage deep Nordlander P, Halas NJ. Nanoparticles heat through light
neural networks in novel chromium-based alloys. Phys localization. Nano Lett. 2014;14(8):4640-4645.
Chem Chem Phys. 2023;25(23):15970-15987. doi: 10.1021/nl5016975
doi: 10.1039/D3CP00402C 50. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-
42. Wei J, Chu X, Sun XY, et al. Machine learning in materials learn: Machine learning in Python. J Mach Learn Res.
science. InfoMat. 2019;1(3):338-358. 2011;12:2825-2830.
doi: 10.1002/inf2.12028 51. Smola AJ, Schölkopf B. A tutorial on support vector
regression. Stat Comput. 2004;14(3):199-222.
43. Ng WL, Goh GL, Goh GD, Ten JS, Yeong WY. Progress
and opportunities for machine learning in materials doi: 10.1023/B: STCO.0000035301.49549.88
and processes of additive manufacturing. Adv Mater. 52. Breiman L. Random forests. Mach Learn. 2001;45(1):5-32.
2024;e2310006.
doi: 10.1023/A:1010933404324
doi: 10.1002/adma.202310006
53. Singh AV, Bhardwaj P, Upadhyay AK, et al. Navigating
44. Huang X, Ng WL, Yeong WY. Predicting the number
of printed cells during inkjet-based bioprinting process regulatory challenges in molecularly tailored nanomedicine.
based on droplet velocity profile using machine learning Explor Biomat X. 2024;1(2):124-134.
approaches. J Intell Manuf. 2023;35:2349-2364. doi: 10.37349/ebmx.2024.00009

Volume 1 Issue 2 (2024) 75 doi: 10.36922/ijamd.3807