The synergy of artificial intelligence and nanotechnology towards advancing innovation and sustainability - A mini-review

• AI enables optimization of nanomanufacturing processes, enhancing efficiency and precision in the production of nanoscale devices and materials. • AI-driven predictive modeling enables the simulation of complex nanoscale phenomena, aiding in the design and optimization of nanomaterials and devices. • AI plays a pivotal role in accelerating materials discovery, optimizing nanomaterial properties for various applications from electronics to healthcare. The convergence of Artificial Intelligence (AI) and nanotechnology is a transformative frontier, holding vast potential for scientific and technological advancements. This review explores the integration of AI and nanotechnology, aiming to uncover current trends, challenges, and future directions across various domains. A scientometric approach was employed, gathering and analyzing existing literature and research in the fields of AI and nanotechnology. Through a systematic examination of recent studies and developments, we identified key trends, challenges, and potential pathways for future research and applications. Findings from this review highlight the significant impact of AI-driven nanotechnology in materials discovery and design, healthcare, environmental monitoring, energy conversion, and quantum computing. These areas have witnessed remarkable advancements, such as the development of novel nanomaterials, precise drug delivery systems, advanced nanosensors, and efficient energy storage solutions. However, challenges persist, including insufficient interdisciplinary collaboration, data quality, ethical concerns, and regulatory frameworks. While challenges require careful consideration and mitigation, the potential for innovation and progress in diverse domains is undeniable. This review underscores the importance of responsible AI use and ongoing research to unlock the full potential of this convergence, ultimately shaping the future of science and technology.

Authors

Institutions

Publication Details

Journal
Nano Trends
Published
2024-09-30
DOI
https://doi.org/10.1016/j.nwnano.2024.100052
Citations
66
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
7.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The synergy of artificial intelligence and nanotechnology towards advancing innovation and sustainability - A mini-review

Adedapo O. Adeola, Abimbola G. Olaremu, Abimbola O. Ige, David B. Olawade et al.
66 citations
Nano Trends
Machine Learning in Materials Science
7.73
article

The synergy of artificial intelligence and nanotechnology towards advancing innovation and sustainability - A mini-review

Adedapo O. Adeola, Abimbola G. Olaremu, Abimbola O. Ige, David B. Olawade, James Ijiwade
article en
66 citations

Abstract

• AI enables optimization of nanomanufacturing processes, enhancing efficiency and precision in the production of nanoscale devices and materials. • AI-driven predictive modeling enables the simulation of complex nanoscale phenomena, aiding in the design and optimization of nanomaterials and devices. • AI plays a pivotal role in accelerating materials discovery, optimizing nanomaterial properties for various applications from electronics to healthcare. The convergence of Artificial Intelligence (AI) and nanotechnology is a transformative frontier, holding vast potential for scientific and technological advancements. This review explores the integration of AI and nanotechnology, aiming to uncover current trends, challenges, and future directions across various domains. A scientometric approach was employed, gathering and analyzing existing literature and research in the fields of AI and nanotechnology. Through a systematic examination of recent studies and developments, we identified key trends, challenges, and potential pathways for future research and applications. Findings from this review highlight the significant impact of AI-driven nanotechnology in materials discovery and design, healthcare, environmental monitoring, energy conversion, and quantum computing. These areas have witnessed remarkable advancements, such as the development of novel nanomaterials, precise drug delivery systems, advanced nanosensors, and efficient energy storage solutions. However, challenges persist, including insufficient interdisciplinary collaboration, data quality, ethical concerns, and regulatory frameworks. While challenges require careful consideration and mitigation, the potential for innovation and progress in diverse domains is undeniable. This review underscores the importance of responsible AI use and ongoing research to unlock the full potential of this convergence, ultimately shaping the future of science and technology.

Nano TrendsVol. 8
University of East London (GB), University of Ibadan (NG), Adekunle Ajasin University (NG), Medway NHS Foundation Trust (GB), University of Minnesota, Duluth (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Machine Learning in Materials Science
7.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.