Page 26 - IJAMD-1-2

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

100. Wang R, Lin TS, Johnson JA, Olsen BD. Kinetic Monte Artificial intelligence in chemistry: Current trends and
Carlo simulation for quantification of the gel point of future directions. J Chem Inf Model. 2021;36(9):3197-3212.
polymer networks. ACS Macro Lett. 2017;6(12):1414-1419.
doi: 10.1021/acs.jcim.1c00619
doi: 10.1021/acsmacrolett.7b00586
109. Kamkar M, Leonard KC, Ferrer I, et al. Artificial intelligence
101. Zhong M, Wang R, Kawamoto K, Olsen BD, Johnson JA. (AI) for sustainable resource management and chemical
Quantifying the impact of molecular defects on polymer processes. ACS Sustain Chem Eng. 2024;12(8):2924-2926.
network elasticity. Science. 2016;353(6305):1264-1268.
doi: 10.1021/acssuschemeng.4c01004
doi: 10.1126/science.aag0184
110. Kostal J. Making the case for quantum mechanics in
102. Vieira AC, Marques AT, Guedes RM, Tita V. Material predictive toxicology-nearly 100 years too late? Chem Res
model proposal for biodegradable materials. Procedia Eng. Toxicol. 2023;36(9):1444-1450.
2011;10:1597-1602.
doi: 10.1021/acs.chemrestox.3c00171
doi: 10.1016/j.proeng.2011.04.267
111. For chemists, the AI revolution has yet to happen. Nature.
103. Jiang S, Liang Y, Shi S, Wu C, Shi Z. Improving predictions 2023;617(7961):438.
and understanding of primary and ultimate biodegradation doi: 10.1038/d41586-023-01612-x
rates with machine learning models. Sci Total Environ.
2023;904:166623. 112. Open Data and AI: A Symbiotic Relationship for Progress.
Available from: https://data.europa.eu/en/publications/
doi: 10.1016/j.scitotenv.2023.166623
datastories/open-data-and-ai-symbiotic-relationship-
104. Wang HSH, Yao Y. Machine learning for sustainable progress [Last accessed on 2024 Mar 07].
development and applications of biomass and biomass- 113. Gormley AJ, Webb MA. Machine learning in combinatorial
derived carbonaceous materials in water and agricultural polymer chemistry. Nat Rev Mater. 2021;6(8):642-644.
systems: A review. Resour Conserv Recycl. 2023;190:106847.
doi: 10.1038/s41578-021-00282-3
doi: 10.1016/j.resconrec.2022.106847
114. Hong S, Liow CH, Yuk JM, et al. Reducing time to discovery:
105. Albright VC, Chai Y. Knowledge gaps in polymer biodegradation Materials and molecular modeling, imaging, informatics,
research. Environ Sci Technol. 2021;55:11476-11488.
and integration. ACS Nano. 2021;15(3):3971-3995.
doi: 10.1021/acs.est.1c00994
doi: 10.1021/acsnano.1c00211
106. Wilson AN, St John PC, Marin DH, et al. PolyID: Artificial 115. Chen G, Shen Z, Iyer A, et al. Machine-learning-assisted de
intelligence for discovering performance-advantaged and novo design of organic molecules and polymers: Opportunities
sustainable polymers. Macromolecules. 2023;56(21):8547-8557.
and challenges. Polymers (Basel). 2020;12(1):163.
doi: 10.1021/acs.macromol.3c00994
doi: 10.3390/polym12010163
107. Roch LM, Häse F, Kreisbeck C, et al. ChemOS: 116. Goh GD, Lee JM, Goh GL, Huang X, Lee S, Yeong WY.
Orchestrating autonomous experimentation. Sci Robot. Machine learning for bioelectronics on wearable and
2018;3(19):eaat5559.
implantable devices: Challenges and potential. Tissue Eng
doi: 10.1126/scirobotics.aat5559 Part A. 2023;29(1-2):20-46.
108. Baum ZJ, Yu X, Ayala PY, Zhao Y, Watkins SP, Zhou Q. doi: 10.1089/ten.tea.2022.0119

Volume 1 Issue 2 (2024) 20 doi: 10.36922/ijamd.3173