Predictive Analysis of use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century

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Shubhrajyotsna Aithal
Aithal P. S.

Abstract

Purpose: The 21st century has seen an unprecedented surge in nanomaterials research, driven by conventional scientific approaches and the advent of potent AI-based tools. This paper focus on comparative analysis, scrutinizing the trajectory of nanomaterial breakthroughs achieved with and without the integration of AI-based Generative Pre-trained Transformers (GPTs). Historically, advances in nanomaterials have occurred during several historical periods, characterized by the discovery of materials like carbon nanotubes, metamaterials, and self-assembling nanostructures. These turning points, which depended on simulations and testing, influenced a variety of fields, including materials science, electronics, and medicine. On the other hand, the age enabled by AI-based GPTs saw a rapid improvement in fields such as artificial intelligence (AI) assisted material design, predictive simulations, automation of synthesis processes, and the development of self-learning nanomaterials and AI-driven nanorobots.


Methodology: This paper uses exploratory research methodology to analyse, compare, evaluate, interpret, and create new knowledge to address the use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century by collecting relevant information using appropriate keywords through Google, Google scholar, and AI-driven GPT search engines.


Analysis & Discussion: When comparing the timelines, research procedures, and material design were significantly expedited by the inclusion of AI-based GPTs. In addition to accelerating discoveries, automation and AI-driven approaches reduced research expenses, which may democratize access to nanotechnology. These GPTs delved into uncharted chemical territory, discovering new compounds with uses in electronics, energy, and medicine. However, issues with data accessibility, bias in AI models, and moral questions about self-learning nanomaterials continue to be crucial topics that demand close attention in order to make responsible and fair progress.


Originality/Value: AI-based GPTs stand as transformative catalysts in nanomaterials research, complementing traditional methodologies. While their integration promises accelerated progress, the responsible and beneficial evolution of AI-powered nanotechnology mandates addressing challenges related to data, bias, and ethical implications for a sustainable future in this burgeoning field.

Article Details

How to Cite
Shubhrajyotsna Aithal, & Aithal P. S. (2024). Predictive Analysis of use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century. International Journal of Applied Engineering and Management Letters (IJAEML), 8(2), 131–144. https://doi.org/10.47992/IJAEML.2581.7000.0226
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