Institute for Materials Science, University of Stuttgart, Germany
Understanding complex metallic materials from accurate ab initio thermodynamics and diffusion data: Physics and workflows
Accurate ab initio thermodynamics and diffusion data play a pivotal role in comprehensively understanding complex metallic materials, encompassing their thermal and chemical behavior. Leveraging the finite-temperature free energy approach accelerated by machine learning potentials, our high-accuracy ab initio data reveal the profound influence of thermal vibrations in particular anharmonicity on various defects [1-3] and diffusion rates . Comprehensive predictions necessitate the thorough consideration of all relevant thermal effects, including also electronic  and magnetic excitations  and their intricate interplay with vibrations .
Going beyond pure metals to concentrated multicomponent alloys presents a formidable challenge in characterizing environment-dependent properties via ab initio methods. We emphasize the importance of employing statistical analysis to extract physically correct properties, such as vacancy energetics . To capture phase transitions and diffusion phenomena accurately, we advocate more effective methodologies, such as on-lattice cluster expansion coupled with Monte Carlo or kinetic Monte Carlo simulations . These techniques enable a holistic consideration of the short-range order effect and the intricate interplay between thermal and chemical factors in concentrated alloys. Based on our predictions, new physical insights regarding the well-known sluggish diffusion in high entropy alloys have been obtained [7,9].
Our high-accuracy ab initio thermodynamics and diffusion data, along with their associated workflows, can be in the future integrated into advanced integrated development environments (IDEs) like Pyiron. This integration will facilitate to expedite the discovery of novel materials through high-throughput screening processes.