Max Planck scientists explore the possibilities of artificial intelligence in materials science and publish their review in the journal Nature Computational Science. Advanced materials become increasingly complex due to the high requirements they have to fulfil regarding sustainability and applic
Merging physics-based simulations with artificial intelligence gains increasing importance in materials science, especially for the design of complex materials that meet technological and environmental demands. Credit: T. You, Max-Planck-Institut für Eisenforschung GmbH
Advanced materials are urgently needed for everyday life, be it in high technology, mobility, infrastructure, green energy or medicine. However, traditional ways of discovering and exploring new materials encounter limits due to the complexity of chemical compositions, structures and targeted properties. Moreover, new materials should not only enable novel applications, but also include sustainable ways of producing, using and recycling them.
“Our means of designing new materials rely today exclusively on physics-based simulations and experiments. This approach can experience certain limits when it comes to the quantitative prediction of high-dimensional phase equilibria and particularly to the resulting non-equilibrium microstructures and properties. Moreover, many microstructure- and property-related models use simplified approximations and rely on a large number of variables.
“AI is capable of automatically extracting thermodynamic and microstructural features from large data sets obtained from electronic, atomistic and continuum simulations with high predictive power,” says Professor Jörg Neugebauer, director at MPIE and co-author of the publication.As the predictive power of artificial intelligence depends on the availability of large data sets, ways of overcoming this obstacle are needed.
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