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

Mikhail A Soldatov,Andrey V Chernov,Pavel V Medvedev,Alexander V Soldatov,Danil Pashkov,Vera V Butova,Maria A Butakova

doi:10.3390/nano11030619

Mikhail A Soldatov, Andrey V Chernov + Show 5 more

Open Access

https://doi.org/10.3390/nano11030619

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Journal: Nanomaterials	Publication Date: Mar 2, 2021
Citations: 31	License type: CC BY 4.0

Affiliation: Southern Federal University

Abstract

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.

Highlights

There is a long history of laboratory synthetic chemistry automation. Such automation designs as continuous flow reactors, single-batch reactors, single-robotic synthesizers, dual-robotic synthesizers, and integrated workstations have already existed for more than three decades
Conventional high-throughput experimentation strategies for material discovery are already used in modern research laboratories [2]
To go far beyond the standard approaches for human-operated laboratories for chemical synthesis, the novel concept of an Artificial intelligence (AI)-driving laboratory has started during recent years

Summary

Introduction

There is a long history of laboratory synthetic chemistry automation. Such automation designs as continuous flow reactors, single-batch reactors, single-robotic synthesizers, dual-robotic synthesizers, and integrated workstations have already existed for more than three decades. Conventional high-throughput experimentation strategies for material discovery are already used in modern research laboratories [2]. To go far beyond the standard approaches for human-operated laboratories for chemical synthesis, the novel concept of an AI-driving laboratory has started during recent years. The ideology of cloud chemistry (i.e., conducting on-line operated chemical experiments using equipment located in someone else’s laboratory in analogy with “cloud computing”) [5] could result in much more effective use of unique and expensive equipment. Digitized complete protocols of the experiments performed in a fully automated laboratory are a very important advantage

Artificial Intelligence and Machine Learning Role in Materials Discovery

AI Applications in the Synthesis

Go Beyond

Findings

Conclusions