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Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning.

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Název:	Recent Advancements in the Field of Nanotechnology Using Artificial Intelligence, Data Science and Machine Learning.
Autoři:	Chimkode, Suresh¹, Mane, Jayant², Jakhete, Mayur³, Moharil, Umesh⁴, Sedani, Chetankumar⁵, Rao, Nandkishore⁶, Garad, Rohit⁷, Rupanar, Shobha⁷, Kakade, Nirmala⁸, Choudhari, Ramakant⁹, Bhambere, Vijay¹⁰, Modak, Girish¹¹, Paliwal, Roma¹², Desai, Smita¹³, Maniyar, Kamalkishor¹³ advancedresearch25@gmail.com, Patil, Ganesh¹³, Mishra, Ekta¹³, Ghodke, Prasad¹⁴, Dixit, Swati¹³, Yadav, Mahendra¹⁵
Zdroj:	Journal of Mines, Metals & Fuels. Jul2025, Vol. 73 Issue 7, p1987-1994. 8p.
Témata:	ARTIFICIAL intelligence, COMPUTER engineering, ELECTRONIC data processing, COMPUTER performance, *COMPUTER engineers
Abstrakt:	Rapidly developing technologies like nanotechnology and machine learning (ML) have many practical uses in data processing, computer engineering, materials research, and medicine. By making data processing easier in the synthesis of nanomaterials, characterisation, and optimization of nanoscale features, machine learning (ML) advances nanotechnology. ML algorithms, on the other hand, depend on faster and more efficient processing power, which nanotechnology enhances. A survey of the academic literature offers insights into the fascinating horizons of both nanotechnology and machine learning, despite the fact that both areas' capabilities are still in their infancy. It also suggests that their integration can have transformational effects. Creating instruments for working with nanomaterials and making sure that data collecting for ML models is impartial and moral are two areas of future research. Furthermore, nanochips can boost the performance and power of contemporary computers, which could cut down on the time and expense required to train intricate machine learning models. The goal is to draw attention to how important it is for these two domains to work together, since this can improve computing power, optimize model training, and hasten nanotechnology research and nanomachine development. It is anticipated that a deeper comprehension of this cross-collaboration will enhance computer capabilities to maximize model training, accelerate nanotechnology research, and guide future nanomachines. Major Findings: This information provides a broad overview of nanotechnology, emphasising both the worldwide concerns regarding their application and the importance of these fields in the development of new systems and technologies. These instances highlight machine learning's revolutionary potential in finding nanoparticles with desired characteristics and streamlining time-consuming procedures in nanotechnology [ABSTRACT FROM AUTHOR]
Databáze:	Academic Search Index

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Abstrakt:	Rapidly developing technologies like nanotechnology and machine learning (ML) have many practical uses in data processing, computer engineering, materials research, and medicine. By making data processing easier in the synthesis of nanomaterials, characterisation, and optimization of nanoscale features, machine learning (ML) advances nanotechnology. ML algorithms, on the other hand, depend on faster and more efficient processing power, which nanotechnology enhances. A survey of the academic literature offers insights into the fascinating horizons of both nanotechnology and machine learning, despite the fact that both areas' capabilities are still in their infancy. It also suggests that their integration can have transformational effects. Creating instruments for working with nanomaterials and making sure that data collecting for ML models is impartial and moral are two areas of future research. Furthermore, nanochips can boost the performance and power of contemporary computers, which could cut down on the time and expense required to train intricate machine learning models. The goal is to draw attention to how important it is for these two domains to work together, since this can improve computing power, optimize model training, and hasten nanotechnology research and nanomachine development. It is anticipated that a deeper comprehension of this cross-collaboration will enhance computer capabilities to maximize model training, accelerate nanotechnology research, and guide future nanomachines. Major Findings: This information provides a broad overview of nanotechnology, emphasising both the worldwide concerns regarding their application and the importance of these fields in the development of new systems and technologies. These instances highlight machine learning's revolutionary potential in finding nanoparticles with desired characteristics and streamlining time-consuming procedures in nanotechnology [ABSTRACT FROM AUTHOR]
ISSN:	00222755
DOI:	10.18311/jmmf/2025/49272