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Advancing sustainability: Biodegradable electronics and materials discovery through artificial intelligence

Mahboubeh Motadayen1 ,  Nehru Devabharathi1 ,  Shweta Agarwala1*
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1 Department of Electrical and Computer Engineering, Faculty of Technical Sciences, Aarhus University, Finlandsgade, Aarhus, Denmark
IJAMD 2024, 1(2), 1–20; https://doi.org/10.36922/ijamd.3173
Submitted: 15 March 2024 | Accepted: 30 May 2024 | Published: 3 July 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The pressing need for sustainable materials and devices stems from growing environmental concerns and the imperative to mitigate climate change. Traditional materials and devices often rely on non-renewable resources and generate significant waste and pollution throughout their lifecycle. By prioritizing sustainability in material and device design, we can foster innovation, promote circular economies, and build a greener future for generations to come. Artificial intelligence (AI) and machine learning (ML) can analyze vast datasets to identify novel materials with desirable properties by reducing the experimental workload. In this paper, we explore the synergistic relationship between sustainable materials discovery and ML models. By leveraging advanced algorithms, researchers can efficiently explore vast chemical spaces to identify environmentally friendly materials with tailored properties. ML techniques, including predictive modeling and generative models, facilitate the rapid discovery and optimization of sustainable materials for various applications, ranging from renewable energy technologies to eco-friendly consumer products. We present a landscape view of the field with a focus on the most recent developments, focusing mainly on transitory materials such as metals, polymers, and semiconducting materials. Furthermore, classification and regression techniques to model the degradation behavior of polymers have been addressed, pointing to key challenges and proposing solutions for enhanced ML applications. The paper discusses the challenges of scaling up data-driven technologies from small molecules to polymers, underscoring AI’s role in discovering new molecular designs and optimizing existing ones for novel applications. It emphasizes the importance of defining and standardizing polymer systems to enable ML models to create a unified data collection system for AI and automation enhancements. Furthermore, it stresses the necessity of refining ML methods to harness the benefits of data-driven polymer chemistry fully, emphasizing the importance of reliable and diverse datasets for predictive models in polymer synthesis.

Keywords
Biodegradability
Machine learning
Artificial intelligence
Transient electronics
Sustainability
Biodegradable polymers
Funding
This work was supported by the Carlsberg Foundation (CF22-0993) and Villum young investigator grant (37508).
Conflict of interest
Shweta Agarwala is an Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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