Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives
Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Nanomaterials |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-4991/12/15/2646 |
| _version_ | 1797432782424637440 |
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| author | Georgios Konstantopoulos Elias P. Koumoulos Costas A. Charitidis |
| author_facet | Georgios Konstantopoulos Elias P. Koumoulos Costas A. Charitidis |
| author_sort | Georgios Konstantopoulos |
| collection | DOAJ |
| description | Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials. |
| first_indexed | 2024-03-09T10:06:39Z |
| format | Article |
| id | doaj.art-8adca03d7aeb4679a9c5dbf0459adefe |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-09T10:06:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-8adca03d7aeb4679a9c5dbf0459adefe2023-12-01T23:04:35ZengMDPI AGNanomaterials2079-49912022-08-011215264610.3390/nano12152646Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green PerspectivesGeorgios Konstantopoulos0Elias P. Koumoulos1Costas A. Charitidis2RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, GreeceInnovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, BelgiumRNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR15773 Athens, GreeceMachine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.https://www.mdpi.com/2079-4991/12/15/2646nanomaterialsartificial intelligencemachine learningin silico design of materialsdata-driven engineeringmanufacturing |
| spellingShingle | Georgios Konstantopoulos Elias P. Koumoulos Costas A. Charitidis Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives Nanomaterials nanomaterials artificial intelligence machine learning in silico design of materials data-driven engineering manufacturing |
| title | Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives |
| title_full | Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives |
| title_fullStr | Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives |
| title_full_unstemmed | Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives |
| title_short | Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives |
| title_sort | digital innovation enabled nanomaterial manufacturing machine learning strategies and green perspectives |
| topic | nanomaterials artificial intelligence machine learning in silico design of materials data-driven engineering manufacturing |
| url | https://www.mdpi.com/2079-4991/12/15/2646 |
| work_keys_str_mv | AT georgioskonstantopoulos digitalinnovationenablednanomaterialmanufacturingmachinelearningstrategiesandgreenperspectives AT eliaspkoumoulos digitalinnovationenablednanomaterialmanufacturingmachinelearningstrategiesandgreenperspectives AT costasacharitidis digitalinnovationenablednanomaterialmanufacturingmachinelearningstrategiesandgreenperspectives |