A new report from market analyst IDTechEx suggests that in future, all materials scientists and chemists will have access to machine learning tools to improve their R&D.
According to Dr Richard Collins, principal analyst at the company, the use of materials informatics (MI) as a data-centric approaches to materials science will become a common method in a research scientist toolkit, with success particularly in organometallics, thermoelectrics, nanomaterials, and ceramics. The report suggests that soft materials such as foams, lubricants, adhesives and polymers will see early success.
‘Additive Manufacturing is an area rapidly embracing MI approaches for both polymer and metallic materials,’ a press release said. ‘This is a rapidly emerging technology and IDTechEx forecast that the materials will be the dominant source of annual revenue. Many have leveraged existing materials for the processes, but developing feedstocks bespoke for the unique processes can help unlock the full potential of this sector.’
Dr Collins suggests that the development of alloys, such as high-temperature nickel-superalloys, will also benefit from machine learning.
This story uses material from IDTechEx, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier.
