Material Informatics (MI) is a data-centric approach applicable to specific material science and chemistry R&D. Without a doubt, this will become a standard method in a research scientist toolkit.
FREMONT, CA: Machine learning has rapidly become an essential part of every industry. Material scientists and chemists will all have access to machine learning tools to enhance their Research & Development in the future. Seamlessly integrating these underlying operations will not happen quickly, but overlooking the developments in materials informatics will lead to a loss of competitive advantage.
Material Informatics (MI) is a data-centric approach applicable to specific material science and chemistry R&D. Without a doubt, this will become a standard method in a research scientist toolkit. Instead of just grabbing headlines, some form of MI will be assumed in all developments. The key to MI is around the integration, implementation, and manipulation of data infrastructures as well as machine learning approaches designed for chemical and materials datasets.
There is a significant amount of evidence to support this. However, the best backing is how the industries are responding to the technology. There has been a large amount of activity over recent years, including partnerships, investments, and announcements from some of the most notable chemical and materials companies.
Machine learning, by itself, can be used in various kinds of projects, from finding new structure-property relationships, proposing new candidates or process conditions, reducing the number of expensive and time-consuming computer simulations, and more. Machine learning approaches can take numerous forms of supervised and unsupervised learning methods. Generative methods can be effective at screening for optimized outputs across organic compounds. At the same time, even simple modified random forest models can be useful for proposing follow-on reactions to meet a desired set of criteria.
However, this is still at an early stage and requires a lot more development. There is a lot to be leveraged from existing developments in AI, but will first require integrating specialist domain knowledge and coping with the unique challenges of a materials dataset. The application space is broad, and studies have shown success ranging from organometallics, thermoelectrics, nanomaterials, and ceramics to many more.