Page 7 - IJAMD-1-2

International Journal of AI for
Materials and Design

REVIEW ARTICLE
Advancing sustainability: Biodegradable

electronics and materials discovery through
artificial intelligence

Mahboubeh Motadayen , Nehru Devabharathi , and Shweta Agarwala*

Department of Electrical and Computer Engineering, Faculty of Technical Sciences, Aarhus
University, Finlandsgade, Aarhus, Denmark

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
*Corresponding author: from renewable energy technologies to eco-friendly consumer products. We present
Shweta Agarwala a landscape view of the field with a focus on the most recent developments, focusing
(shweta@ece.au.dk)
mainly on transitory materials such as metals, polymers, and semiconducting materials.
Citation: Motadayen M, Furthermore, classification and regression techniques to model the degradation
Devabharathi N, Agarwala
S. Advancing sustainability: behavior of polymers have been addressed, pointing to key challenges and proposing
Biodegradable electronics and solutions for enhanced ML applications. The paper discusses the challenges of scaling
materials discovery through artificial up data-driven technologies from small molecules to polymers, underscoring AI’s role in
intelligence. Int J AI Mater Design.
2024;1(2):3173. discovering new molecular designs and optimizing existing ones for novel applications.
doi: 10.36922/ijamd.3173 It emphasizes the importance of defining and standardizing polymer systems to
Received: March 15, 2024 enable ML models to create a unified data collection system for AI and automation
Accepted: May 30, 2024 enhancements. Furthermore, it stresses the necessity of refining ML methods to harness
Published Online: July 3, 2024 the benefits of data-driven polymer chemistry fully, emphasizing the importance of
Copyright: © 2024 Author(s). reliable and diverse datasets for predictive models in polymer synthesis.
This is an Open-Access article
distributed under the terms of the
Creative Commons Attribution Keywords: Biodegradability; Machine learning; Artificial intelligence; Transient
License, permitting distribution, electronics; Sustainability; Biodegradable polymers
and reproduction in any medium,
provided the original work is
properly cited.
Publisher’s Note: AccScience
Publishing remains neutral with 1. Introduction
regard to jurisdictional claims in
published maps and institutional Sustainable development is an approach of utilizing resources that strives to fulfill the
affiliations. needs of humans, while also protecting the environment so that they can be addressed

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