scholarly journals Autonomous Discovery in the Chemical Sciences Part II: Outlook

2020 ◽  
Vol 59 (52) ◽  
pp. 23414-23436 ◽  
Author(s):  
Connor W. Coley ◽  
Natalie S. Eyke ◽  
Klavs F. Jensen
Keyword(s):  
2020 ◽  
Vol 3 (6) ◽  
pp. 470-470
Author(s):  
Felix T. Bölle ◽  
Nicolai R. Mathiesen ◽  
Alexander J. Nielsen ◽  
Tejs Vegge ◽  
Juan Maria Garcia‐Lastra ◽  
...  

2020 ◽  
Vol 3 (6) ◽  
pp. 473-473
Author(s):  
Felix T. Bölle ◽  
Nicolai R. Mathiesen ◽  
Alexander J. Nielsen ◽  
Tejs Vegge ◽  
Juan Maria Garcia‐Lastra ◽  
...  

Nanomaterials ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Maria A. Butakova ◽  
Andrey V. Chernov ◽  
Oleg O. Kartashov ◽  
Alexander V. Soldatov

Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters’ experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter’s behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.


Author(s):  
Loı̈c M. Roch ◽  
Florian Häse ◽  
Christoph Kreisbeck ◽  
Teresa Tamayo-Mendoza ◽  
Lars P. E. Yunker ◽  
...  

<div>Autonomous or “self-driving” laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.</div>


2020 ◽  
Vol 59 (51) ◽  
pp. 22858-22893 ◽  
Author(s):  
Connor W. Coley ◽  
Natalie S. Eyke ◽  
Klavs F. Jensen
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document