Publication date: 15th December 2025
Despite significant progress, computational materials design still faces major challenges—particularly when simulating advanced and chemically complex materials with the accuracy of density functional theory (DFT) or beyond.[1] To overcome these limitations, machine learning (ML) methods have gained considerable traction in recent years.
We have developed robust data-generation strategies to support the creation and benchmarking of new ML models.[2] In this talk, I will highlight methods for large-scale quantum-chemical bonding analysis and workflows for ML interatomic potentials.
Our work demonstrates that quantum-chemical bonding properties can be incorporated into ML models to predict phononic properties.[3] This approach enables large-scale validation of expected correlations—such as the link between bonding strength and force constants or thermal conductivities.
Furthermore, we have built an automated training framework for machine-learned interatomic potentials (autoplex).[4] Initial workflows include random structure searches, suitable for general-purpose potentials, as well as specialized workflows targeting ML potentials with accurate phonon properties.
While atomistic simulations are highly effective for certain material properties, others—such as magnetism or synthesizability—remain challenging. In these cases, promising strategies include benchmarking established ab initio methods against chemical heuristics or developing new ML models primarily based on experimental data.[5,6]
J.G. was supported by ERC Grant MultiBonds (grant agreement No 101161771; Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.)
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