Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives DOI Open Access
Amiya Kumar Tripathy, Akshata Y. Patne, Subhra Mohapatra

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(22), P. 12368 - 12368

Published: Nov. 18, 2024

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, data processing. ML enhances nanotechnology by facilitating the processing of dataset nanomaterial synthesis, characterization, optimization nanoscale properties. Conversely, improves speed efficiency computing power, which is crucial for algorithms. Although capabilities still their infancy, a review research literature provides insights into exciting frontiers these suggests that integration can be transformative. Future directions include developing tools manipulating nanomaterials ensuring ethical unbiased collection models. This emphasizes importance coevolution technologies mutual reinforcement to advance scientific societal goals.

Language: Английский

Unexpected structural scaling and predictability in carbon nanotubes DOI
Guohai Chen, Kazufumi Kobashi, Don N. Futaba

et al.

Journal of Material Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

1
A Review of Machine Learning in Organic Solar Cells DOI Open Access
Darya R. Ahmed, Fahmi F. Muhammadsharif

Processes, Journal Year: 2025, Volume and Issue: 13(2), P. 393 - 393

Published: Feb. 1, 2025

Organic solar cells (OSCs) are a promising renewable energy technology due to their flexibility, lightweight nature, and cost-effectiveness. However, challenges such as inconsistent efficiency low stability limit widespread application. Addressing these issues requires extensive experimentation optimize device performance, process hindered by the complexity of OSC molecular structures architectures. Machine learning (ML) offers solution accelerating material discovery optimizing performance through analysis large datasets prediction outcomes. This review explores application ML in advancing technologies, focusing on predicting critical parameters power conversion (PCE), levels, absorption spectra. It emphasizes importance supervised, unsupervised, reinforcement techniques analyzing descriptors, processing data, streamlining experimental workflows. Concludingly, integrating with quantum chemical simulations, alongside high-quality effective feature engineering, enables accurate predictions that expedite efficient stable materials. By synthesizing advancements ML-driven research, gap between theoretical potential practical implementation can be bridged. viably accelerate transition OSCs from laboratory research commercial adoption, contributing global shift toward sustainable solutions.

Language: Английский

Citations

0
Impact of catalyst precursors on nanoparticle formation and carbon nanotube synthesis unveiled by multi-step chemical vapor deposition DOI
Takashi Tsuji, Guohai Chen,

Maho Yamada

et al.

Materials Today Chemistry, Journal Year: 2025, Volume and Issue: 44, P. 102576 - 102576

Published: Feb. 7, 2025

Language: Английский

Citations

0
Convergence of Nanotechnology and Machine Learning: The State of the Art, Challenges, and Perspectives DOI Open Access
Amiya Kumar Tripathy, Akshata Y. Patne, Subhra Mohapatra

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(22), P. 12368 - 12368

Published: Nov. 18, 2024

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, data processing. ML enhances nanotechnology by facilitating the processing of dataset nanomaterial synthesis, characterization, optimization nanoscale properties. Conversely, improves speed efficiency computing power, which is crucial for algorithms. Although capabilities still their infancy, a review research literature provides insights into exciting frontiers these suggests that integration can be transformative. Future directions include developing tools manipulating nanomaterials ensuring ethical unbiased collection models. This emphasizes importance coevolution technologies mutual reinforcement to advance scientific societal goals.

Language: Английский

Citations

2