Page 19 - IJAMD-1-2

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

A B

C

Figure 4. Machine learning (ML)/artificial intelligence (AI) techniques for automating experimental procedures in polymer research. (A) Example of an ML
technique demonstrating the identification of crucial knowledge gaps and proposal of methods to determine polymer degradability and non-degradability.
Reproduced with permission. (B) Schematic illustrating ML-based PolyID tool describing the usage to reduce the design space of renewable feedstocks
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to enable efficient discovery of performance-advantaged, bio-based polymers. Reproduced with permission. (C) The set of instructions provided to
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ChemOS by researchers is interpreted through a natural language processing module. New experiments to be assessed on the automated robotics platforms
are suggested by the AI algorithm. The outcomes of the experiments are gathered and employed to enhance the AI model of the ongoing experiment in a
closed-loop approach. Reproduced with permission. 108
in discovering sustainable materials with enhanced in the green and sustainable domains, the AI experts
performance. In addition, AI algorithms are used with expedite the development of the circular economy. The
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automated robotics platforms to create autonomous synergy between ML algorithms and material science is
laboratories to conduct experiments independently. revolutionizing the discovery and development of new
AI designs and recommends experiments, validated materials. As a result, through simulated screening,
by robotics platforms, and analyzes results to enhance researchers may evaluate new materials more effectively,
experimental strategies and propose better hypotheses reducing the need for experimental and computing
for subsequent experiments (Figure 4C). 107 resources. AI-assisted materials discovery represents
a paradigm shift in scientific exploration, leveraging
7. AI-assisted materials discovery advanced algorithms and data analytics to expedite the

AI possesses the capacity to become a revolutionary identification and development of novel materials with
force, catalyzing progress across diverse scientific and tailored properties. By harnessing the power of ML and
technical domains, including chemistry, materials computational modeling, researchers can efficiently
science, and engineering. The ability of AI to extract navigate vast chemical spaces, accelerating the pace of
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insights from complex systems has been shown to innovation and opening new avenues for breakthroughs
boost productivity, lower capital costs, and improve in various industries such as healthcare, energy, and
product quality and user satisfaction. However, AI’s electronics. This interdisciplinary approach not only
actual worth is grounded in its ability to advance enhances our understanding of materials at the atomic
scientific discoveries and provide answers to difficult level but also promises to revolutionize the way we
worldwide issues related to the global environment, design and engineer materials for diverse applications,
economy, and society. Bringing together specialists ultimately leading to the creation of next-generation
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Volume 1 Issue 2 (2024) 13 doi: 10.36922/ijamd.3173