Materials Informatics is the key to enabling a paradigm shift in our approach to materials science R&D. Significant investment, notable adoption from key end-users, and technological leaps make it evident that its time has come.
What Is Materials Informatics?
Materials Informatics (MI) is the use of data-centric approaches for the advancement of materials science. This can take numerous forms and influence all parts of R&D (hypothesis, data handling & acquisition, data analysis, knowledge extraction).
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 optimization of how they are processed. MI can accelerate the “forward” direction of innovation (properties are realized for an input material), but the idealized solution is to enable the “inverse” direction (materials are designed given desired properties).
This is not straightforward and is still at an evolving stage. In some cases, the data infrastructure is not comprehensive and ML algorithms can be immature for the given experimental data. The challenge is not the same as in other AI-led areas (such as autonomous cars or social media), the players are often dealing with sparse, high-dimensional, biased, and noisy data; leveraging domain knowledge is an essential part of most approaches. If integrated correctly, MI will become a set of enabling technologies accelerating the R&D process for research scientists.
Why Now?
Utilizing MI is not a new approach, many sectors have adopted similar design approaches for decades. But there are three main reasons why this transformative technology is impacting the materials science space right now:
– Improvements in AI-driven solutions leveraged from other sectors.
– Improvements in data infrastructures, from open-access data repositories to cloud-based research platforms.
– Awareness, education, and a need to keep up with the underlying pace of innovation.
There are three significant advantages to employing advanced machine learning techniques into the materials R&D process: enhanced screening of candidates & scoping research areas, reducing the number of experiments to develop a new material (and therefore time to market), and finding new materials or relationships. The training data can be based on internal experimental, computational simulation, or from external data repositories; enhanced laboratory informatics and high throughput experimentation or computation can be integral to many projects.
What Are the Strategic Approaches?
Ignoring this R&D transition is a major oversight for any company that designs materials or designs with materials. The impact will not be seen immediately, but in the mid- to long-term the missed opportunity will be significant. This could be when bringing competitive products to market, developing versatility in the supply chain, finding next-generation opportunities, or generating the ability to diversify a business unit or material portfolio.
What application areas are successfully using this?
Advanced composite materials, battery compositions, additive manufacturing alloys, nanomaterials, and nanocomposites development are all examples of areas in which MI is having an immediate impact. The broad range of material use cases means industrial adoption is being seen from electronics manufacturers to chemical companies.
Find out how you can partner with AI Materia to incorporate MI in your R&D process.
