Materials Science International collaboration Lays the Foundation for AI for Materials
A prerequisite for AI in materials research is large-scale use and exchange of data on materials, which is facilitated by an international standard. New technologies in areas such as energy and sustainability involving for example batteries, solar cells, LED lighting and biodegradable materials require new materials. Many researchers around the world are working to create materials that have not existed before. But there are major challenges in creating materials with the exact properties required, such as not containing environmentally hazardous substances and at the same time being durable enough to not break down.
“We’re now seeing an explosive development where researchers in materials science are adopting AI methods from other fields and also developing their own models to use in materials research. Using AI to predict properties of different materials opens up completely new possibilities,” says Rickard Armiento, associate professor at the Department of Physics, Chemistry and Biology (IFM) at Linköping University in Sweden.
AI that can make predictions
Today, many demanding simulations are performed on supercomputers that describe how electrons move in materials, which gives rise to different material properties. These advanced calculations yield large amounts of data that can be used to train machine learning models. These AI models can then immediately predict the responses to new calculations that have not yet been made, and by extension predict the properties of new materials. But huge amounts of data are required to train the models. “We’re moving into an era where we want to train models on all data that exist,” says Rickard Armiento.
Data from large-scale simulations, and general data about materials, are collected in large databases. Over time, many such databases have emerged from different research groups and projects, like isolated islands in the sea. They work differently and often use properties that are defined in different ways.
“Researchers at universities or in industry who want to map materials on a large scale or want to train an AI model must retrieve information from these databases. Therefore, a standard is needed so that users can communicate with all these data libraries and understand the information they receive”, says Gian-Marco Rignanese, professor at the Institute of Condensed Matter and Nanosciences at UCLouvain in Belgium.
The OPTIMADE (Open databases integration for materials design) standard has been developed ov
