Page 25 - IJAMD-1-2

International Journal of AI for
Materials and Design
Sustainable electronics using AI/ML

of ready biodegradability based on combined public learning models for predicting aerobic ready and inherent
and industrial data sources. SAR QSAR Environ Res. biodegradation of organic chemicals in water. Environ Sci
2020;31(3):171-186. Technol. 2022;56(17):12755-12764.
doi: 10.1080/1062936X.2019.1697360 doi: 10.1021/acs.est.2c01764
79. Czermiński R, Yasri A, Hartsough D. Use of support vector 89. Lee M, Min KA. comparative study of the performance for
machine in pattern classification: Application to QSAR predicting biodegradability classification: The quantitative
studies. Quant Struct Act Relat. 2001;20(3):227-240. structure-activity relationship model vs the graph
doi: 10.1002/1521-3838(200110)20:3<227:AID-QSAR227> convolutional network. ACS Omega. 2022;7(4):3649-3655.
3.0.CO;2-Y doi: 10.1021/acsomega.1c06274
80. Davis CW, Camenzuli L, Redman AD. Predicting primary 90. Cencer MM, Moore JS, Assary RS. Machine learning
biodegradation of petroleum hydrocarbons in aquatic for polymeric materials: An introduction. Polym Int.
systems: Integrating system and molecular structure 2022;71(5):537-542.
parameters using a novel machine-learning framework.
Environ Toxicol Chem. 2022;41(6):1359-1369. doi: 10.1002/pi.6345
91. Lin A, Uva A, Babi J, Tran H. Materials design for resilience
doi: 10.1002/etc.5328
in the biointegration of electronics. MRS Bull. 2021;46:860-
81. Taunk K, De S, Verma S, Swetapadma A. A Brief Review of 869.
Nearest Neighbor Algorithm for Learning and Classification. doi: 10.1557/s43577-021-00174-5
In: 2019 International Conference on Intelligent Computing
and Control Systems (ICCS). IEEE; 2019. p. 1255-1260. 92. Jorgensen RA. Plant science. A window on the sophistication
of plants. Science. 2011;333(6046):1103-1104.
doi: 10.1109/ICCS45141.2019.9065747
doi: 10.1126/science.1211194
82. Zhang XM, Liang L, Liu L, Tang MJ. Graph neural networks
and their current applications in bioinformatics. Front 93. Roth B, Savagatrup S, De Los Santos NV, et al. Mechanical
Genet. 2021;12:690049. properties of a library of low-band-gap polymers. Chem
Mater. 2016;28(7):2363-2373.
doi: 10.3389/fgene.2021.690049
doi: 10.1021/acs.chemmater.6b00525
83. Frazier PI. A Tutorial on Bayesian Optimization, Section
5. 2018. p. 1-22. Available from: http://arxiv.org/ 94. Mei J, Bao Z. Side chain engineering in solution-processable
abs/1807.02811 [Last accessed on 2024 Jul 02]. conjugated polymers. Chem Mater. 2014;26(11):604-615.
84. De Carvalho Rocha WF, Sheen DA. Classification doi: 10.1021/cm4020805
of biodegradable materials using QSAR modeling 95. Abetz V, Simon PFW. Phase behaviour and morphologies of
with uncertainty estimation. SAR QSAR Environ Res. block copolymers. In: Advances in Polymer Science. Berlin:
2016;27(10):799-811.
Springer; 2005. p. 125-212.
doi: 10.1080/1062936X.2016.1238010
doi: 10.1007/12_004
85. Nolte TM, Peijnenburg WJGM, van Bergen TJHM, 96. Sidky H, Chen W, Ferguson AL. Molecular latent space
Hendriks AJ. Transition-state rate theory sheds light on
“Black-Box” biodegradation algorithms. Green Chem. simulators. Chem Sci. 2020;11(35):9459-9467.
2020;22(11):3558-3571. doi: 10.1039/d0sc03635h
doi: 10.1039/D0GC00337A 97. Gu Y, Zhao J, Johnson JA. A (Macro)molecular-level
understanding of polymer network topology. Trends Chem.
86. McDonald SM, Augustine EK, Lanners Q, Rudin C, 2019;1(3):318-334.
Catherine Brinson L, Becker ML. Applied machine learning
as a driver for polymeric biomaterials design. Nat Commun. doi: 10.1016/j.trechm.2019.02.017
2023;14(1):4838.
98. Rubinstein M. Polymer physics-the ugly duckling story: Will
doi: 10.1038/s41467-023-40459-8 polymer physics ever become a part of “Proper” physics? J
Polym Sci B Polym Phys. 2010;48:2548-2551.
87. Jaworska JS, Boethling RS, Howard PH. Recent developments
in broadly applicable structure-biodegradability doi: 10.1002/polb.22135
relationships. Environ Toxicol Chem. 2003;22(8):1710-1723.
99. Göpferich A. Mechanisms of Polymer Degradation and
doi: 10.1897/01-302 Erosion. Vol. 17. Amsterdam: Elsevier; 1996.
88. Huang K, Zhang H. Classification and regression machine doi: 10.1016/B978-008045154-1.50016-2

Volume 1 Issue 2 (2024) 19 doi: 10.36922/ijamd.3173