在 21 世纪纳米材料研究突破中使用人工智能驱动的 GPT 的预测分析

S. Aithal, P. S. Aithal
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引用次数: 0

摘要

目的:在传统科学方法和强大的人工智能工具的推动下,21 世纪的纳米材料研究出现了前所未有的热潮。本文将重点放在比较分析上,仔细研究在集成和未集成基于人工智能的预训练生成变换器(GPT)的情况下,纳米材料取得突破性进展的轨迹。从历史上看,纳米材料的进步发生在以碳纳米管、超材料和自组装纳米结构等材料的发现为特征的几个历史时期。这些转折点依赖于模拟和测试,影响了材料科学、电子学和医学等多个领域。另一方面,在基于人工智能的 GPT 时代,人工智能(AI)辅助材料设计、预测模拟、合成过程自动化以及自学习纳米材料和人工智能驱动的纳米机器人的开发等领域都得到了快速发展。研究方法:本文采用探索性研究方法,通过谷歌、谷歌学者和人工智能驱动的GPT搜索引擎,使用适当的关键词收集相关信息,对人工智能驱动的GPT在21世纪纳米材料研究突破中的应用进行分析、比较、评估、解释并创造新知识。分析与讨论:在比较时间轴、研究程序和材料设计时,基于人工智能的 GPT 明显加快了速度。除了加速发现之外,自动化和人工智能驱动的方法还降低了研究费用,这可能会使纳米技术的获取更加平民化。这些 GPT 探索了未知的化学领域,发现了可用于电子、能源和医药的新化合物。然而,数据的可获取性、人工智能模型的偏差以及有关自学纳米材料的道德问题仍然是需要密切关注的关键议题,以便取得负责任和公平的进展。原创性/价值:基于人工智能的 GPT 是纳米材料研究的变革性催化剂,是对传统方法的补充。虽然它们的整合有望加速进展,但人工智能纳米技术负责任和有益的发展要求解决与数据、偏见和伦理影响有关的挑战,以实现这一新兴领域的可持续未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Analysis of use of AI-Driven GPTs in Nanomaterials Research Breakthroughs in the 21st Century
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.
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