What Materials Informatics Looks Like In Practice

Definitions of materials informatics often focus on the conceptual, but it’s important to understand what the day-to-day applications of it look like. 

Before materials informatics emerged as a field, materials science researchers had limited technology on hand for logging experiment data, sharing it among colleagues, and running advanced analysis on it later. They recorded non-standardized data manually, in notebooks and later spreadsheets, working in silos far removed from their counterparts all over the world—or even from other researchers in their own organization.

Once the scientific community began applying the principles of informatics to materials, they gained several advantages. They started working from standardized, structured datasets of material properties and experiment data connected across disparate organizations and initiatives. They were able to learn from other researchers’ experiments on similar materials, something that was especially difficult in emerging fields. They also had access to more formulations and raw materials data to learn from, making future experiments more efficient.

The industrial materials sector noticed the efforts of their counterparts in academia and began making their own advances. They applied the same data science techniques, machine learning algorithms, and data management strategies that fast-growing startups were using to make waves in the tech world to their own R&D workflows. Though most large companies are yet to have an accessible, fully connected materials database they can use to train machine learning models on, the ones that do can make predictions that let them move much faster than their competitors.

Today many organizations are realizing the value of materials informatics. Industrial manufacturers are using the concepts to do things like:

  • Connecting data in a structured manner across the development pathway, from raw materials, into formulations, and finally including all relevant results and properties
  • Combining and analyzing experiment results with fellow researchers across the globe to get to insights faster
  • Simulating experiments using data like chemical structures and past outcomes instead of having to physically run them
  • Investing in databases and analysis tools that are powerful enough to make intelligent predictions
  • Making R&D data accessible for all stakeholders, including suppliers, customers, and innovation partners without compromising intellectual property
  • Running high-throughput experiments that result in more data without a large financial or time commitment 

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