Page 17 - IJAMD-1-2

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

commonly used because their bulkiness could hinder knowledge to comprehend the chemical basis of polymer
interchain interactions. Simply, adding ionic side chains network architecture. Research in this subject area will
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can also make them more soluble in water and polar continue to harness the topology of polymer networks
organics. Besides, structure-dependent characteristics as the study direction to a greater extent. By employing
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can be fundamentally understood by examining polymer advanced characterization techniques and synthesis
architecture, as it affects intra- and inter-molecular methods, researchers can achieve a better understanding
interactions in melts and solutions. Block copolymer of how network topology influences the overall properties
structural characterization is an intriguing and developing of materials. This understanding could pave the way for
field of study with great potential for advancement. the development of new materials possessing unique
Controlling the morphology at the nanoscale can be mechanical qualities and functionalities that were
achieved by selecting the right blocks in terms of chemistry, previously considered improbable. 97
composition, and architecture. Sidky et al. reported
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using data-driven ML/deep learning-based trajectory 6. ML-guided biodegradation insights
forecasting algorithms to reduce the computational load A large range of biodegradable materials especially
of simulating the complicated structures. ML techniques polymers are used in many products that require
coupled with simulations hold useful potential for a limited lifespan. Every biodegradable polymer
comprehending the underlying relationship between the comprises hydrolyzable or oxidizable bonds. Owing
complex architecture and the characteristics of grafted to this, the material is susceptible to mechanical
polymers. 96 stress, heat, light, and moisture. The various forms of
Many properties of polymer networks are determined polymer degradation – photo, thermal, mechanical,
by their topology. However, due to their amorphous and chemical – can occur singularly or in combination,
nature, they are challenging to control. Various contributing to the degradation. When water molecules
methods can be employed to manipulate the structural are present, the macromolecular chemical bonds may
features of polymer networks at the molecular level. hydrolyze, which can result in chain scissions. These
These techniques include programming topological scissions happen at the ester groups in the case of
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information into network precursors or biassing of aliphatic polyesters. Nonetheless, a key challenge here is
polymerization kinetics. Notably, elasticity is a crucial to understand the mechanical behavior of biodegradable
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characteristic of polymer networks; nevertheless, it is not materials throughout their degradation. For example,
clear how elastically deficient loops affect bulk elasticity. a forecast model is utilized to read the mechanical
Therefore, theories designed to predict elastic modulus properties in a composite of PCL and PLA. Here, a
from the molecular scale have proven challenging to numerical method using ABAQUS® is provided, in which
validate experimentally. In a recent effort to enhance a user material subroutine automatically updates the
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the understanding of the relationship between topology material properties of the proposed model in accordance
and elasticity, Zhong et al. conducted a study to evaluate with the degradation time. This model is claimed to be
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the elasticity of classical phantom and affine network applied to other thermoplastic biodegradable materials
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theories by examining the shear elastic modulus and that exhibit hyperelastic behavior. Yet a study by
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quantifying topological loop defects of various orders in Jiang et al. aimed to develop ML-based models to
a range of polymer hydrogels. This investigation utilized predict primary and ultimate biodegradation rates of
techniques such as rheology, disassembly spectrometry, organic chemicals, which are crucial for environmental
and simulations to gain insights into the interplay risk assessment. The survey offers valuable tools for
between topology and elasticity in these materials. predicting biodegradation rates and insights into
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The effects of loop defects on the elasticity of polymer underlying mechanisms (Figure 3A).
networks are explained by the real elastic network theory Figure 3B shows the application of ML in the
(RENT), which is a modified version of the phantom context of biomass-derived materials for water and
network model. 97,100,101 RENT considers the anticipated agricultural systems. Analyzing and reviewing a
effects of loops of different orders on network elasticity collection of 53 papers published since 2008, Wang
and provides estimates of shear elastic modulus that align and Yao categorized ML applications into material
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with experimental results. On the other hand, there were and process design, end-use performance prediction,
also reports employing kinetic Monte Carlo simulation to and sustainability assessment. The environmental
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characterize growth kinetics and network structure at the fate of polymers, particularly biodegradable ones, has
same time. The effectiveness of this theory in predicting become a focal point across academic, industrial, and
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mechanical properties emphasizes the value of molecular regulatory sectors. Albright and Chai examined test

Volume 1 Issue 2 (2024) 11 doi: 10.36922/ijamd.3173