Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions

Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipm...

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Main Authors: Mikhail A. Soldatov, Vera V. Butova, Danil Pashkov, Maria A. Butakova, Pavel V. Medvedev, Andrey V. Chernov, Alexander V. Soldatov
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Nanomaterials
Subjects:
Online Access:https://www.mdpi.com/2079-4991/11/3/619
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spelling doaj-e8835120fcb548c2a0ae5a8b6962c2592021-03-03T00:04:29ZengMDPI AGNanomaterials2079-49912021-03-011161961910.3390/nano11030619Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known ReactionsMikhail A. Soldatov0Vera V. Butova1Danil Pashkov2Maria A. Butakova3Pavel V. Medvedev4Andrey V. Chernov5Alexander V. Soldatov6The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaThe Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, RussiaInnovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI’s role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories’ self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials’ synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.https://www.mdpi.com/2079-4991/11/3/619self-driving laboratoriesartificial intelligencesynthesisautomatizationoptimization
collection DOAJ
language English
format Article
sources DOAJ
author Mikhail A. Soldatov
Vera V. Butova
Danil Pashkov
Maria A. Butakova
Pavel V. Medvedev
Andrey V. Chernov
Alexander V. Soldatov
spellingShingle Mikhail A. Soldatov
Vera V. Butova
Danil Pashkov
Maria A. Butakova
Pavel V. Medvedev
Andrey V. Chernov
Alexander V. Soldatov
Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
Nanomaterials
self-driving laboratories
artificial intelligence
synthesis
automatization
optimization
author_facet Mikhail A. Soldatov
Vera V. Butova
Danil Pashkov
Maria A. Butakova
Pavel V. Medvedev
Andrey V. Chernov
Alexander V. Soldatov
author_sort Mikhail A. Soldatov
title Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
title_short Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
title_full Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
title_fullStr Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
title_full_unstemmed Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions
title_sort self-driving laboratories for development of new functional materials and optimizing known reactions
publisher MDPI AG
series Nanomaterials
issn 2079-4991
publishDate 2021-03-01
description Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI’s role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories’ self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials’ synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.
topic self-driving laboratories
artificial intelligence
synthesis
automatization
optimization
url https://www.mdpi.com/2079-4991/11/3/619
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