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...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-03-01
|
Series: | Nanomaterials |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-4991/11/3/619 |
id |
doaj-e8835120fcb548c2a0ae5a8b6962c259 |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT mikhailasoldatov selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions AT veravbutova selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions AT danilpashkov selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions AT mariaabutakova selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions AT pavelvmedvedev selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions AT andreyvchernov selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions AT alexandervsoldatov selfdrivinglaboratoriesfordevelopmentofnewfunctionalmaterialsandoptimizingknownreactions |
_version_ |
1724233927599587328 |