Materials informatics (MI) involves the use of data-centric approaches for materials science R&D. There are multiple strategic approaches and already some notable success stories; significant adoption is happening now and missing this transition will be very costly.
Primarily, MI is based on using data infrastructures and leveraging machine learning solutions for the design of new materials, discovery of materials for a given application, or optimisation of how materials are processed. This is not straight-forward and is still at a nascent stage, but with industry engagement, a flourishing start-up scene, and academic progression the future is very bright.
Ultimately, MI will become a valuable enabling toolkit to anyone that develops materials or designs with materials. MI can accelerate the "forward" direction of innovation (properties are realised for an input material), but the idealised solution is to enable the "inverse" direction (materials are designed given desired properties).
Organic electronics, battery compositions, additive manufacturing alloys, polyurethane formulations, and nanomaterial development are all examples of areas that MI is having an immediate impact on. Numerous industrial end-users have already begun this adoption with 3 core approaches: operate fully in-house, work with an external company, or join forces as part of a consortium.
This webinar will provide:
- A technical introduction to the field
- A discussion as to why now?
- An overview of key players and developments
- Key commercial outlooks
- Critical appraisal of strategic direction available to industrial users
- Success stories