Automated robotic platforms in design and development of formulations. Issue 5 (3rd March 2021)

• Record Type: Journal Article
• Authors: Cao, Liwei; Russo, Danilo; Lapkin, Alexei A.
• Is Part Of: AIChE journal. Volume 67:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-03
• Subjects: automation -- formulated products -- machine learning -- product design -- robotics; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17248
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Product design for formulations is an active and challenging area of research. The new challenges of a fast‐paced market, products of increasing complexity, and practical translation of sustainability paradigms require to re‐examine the existing theoretical frameworks to include the advantages deriving from the new reality of digitalization of business and research. In this work, we review the existing approaches, clearly stating the role of automation and machine‐learning‐guided optimization in the broader framework. Moving from this, we review the state of the art of automated hardware and software for formulated product design, and identify the open challenges for future research. Perspectives are given on the emerging fields of automated discovery, scale‐up, and multistage optimization, and a unitary picture of the existing connections is provided, in the general context of a completely digital R&D workflow.