Materials Informatics: Transforming Scientific Research and Discovery
The scientific field of research and discovery has been given an unprecedented boost. According to recent statistics, it costs around £500 million to bring a single drug to market, with the vast majority of finances being spent on trial-and-error experiments.
With Material Informatics, however, the process of scientific research and discovery can be transformed. Instead of spending time and money conducting trial-and-error experiments in the lab, computational scientists can accelerate the process, predicting material properties with data science and machine learning.
This will then allow scientists to conduct systematic searches of possible materials, helping to discover compounds with the optimum properties and even design new materials based on the properties that have been analysed.
So how does it do this, and how could this transform scientific research and discovery forever? To understand that, it’s first important to understand exactly what materials informatics means:
Materials Informatics Explained
In short, materials informatics is the combination of AI and ML to collect and analyse large amounts of material property data – including mechanical behaviour, electronic structure, thermodynamic properties, and more.
It does this by taking the study of computational systems and applying it to the development of materials – while data techniques, AI and ML were once only used in data-rich industries, materials informatics brings that data management to the field of science to accelerate processes and reach new material discovery faster than ever before.
How Is It Transforming Scientific Research And Discovery?
As mentioned before, the way it is changing scientific research and discovery is by making everything far more streamlined and focused. The cost of trial-and-error is spectacular in almost every scientific field, with a conventional route from beginning to end involving several timely steps.
In the beginning, researchers would have to set the target performance of new materials. They would then survey a number of similar development cases to get an idea of where they can go and what they could apply. After this, they would design materials and simulate countless candidate materials, prototyping and evaluating the performance of each one.
When it comes to materials informatics, however, the steps to finding new materials – or developing existing materials – are cut shorter by a few steps. With the use of ML databases and AI to calculate material properties with higher precision, the process starts by setting a performance, selecting the candidate materials (nominated by AI), simulating the candidate materials and then evaluating all of the selected candidates.
The Future Of Science
The impact this could have on the field of science is monumental, not least due to the acceleration of new materials and the reduced cost of finding them. As well as this, AI is set to improve over the next twenty years, which would further help to transform the industry and push research and discovery even further.
As of right now, however, the market size of materials informatics is growing considerably, with more and more scientists understanding the benefits of its application in their own field of study. By 2030, the market is set to reach $1,012 million, growing at a CAGR rate of 26.74%. The future of science, then, is set to reach the next level.
