Xi Zhang

B. Grabowski

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 [4]. Comprehensive predictions necessitate the thorough consideration of all relevant thermal effects, including also electronic [5] and magnetic excitations [6] and their intricate interplay with vibrations [5].

    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 [7]. 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 [8]. 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.

X. Zhang, B. Grabowski, T. Hickel, and J. Neugebauer,
Calculating free energies of point defects from ab initio
Computational Materials Science 148, 249–259 (2018).
X. Zhang, B. Grabowski, F. Körmann, A. V. Ruban, Y. Gong, R. C. Reed, T. Hickel, and J. Neugebauer,
Temperature dependence of the stacking-fault Gibbs energy for Al, Cu, and Ni
Physical Review B 98, 224106 (2018).
A. Forslund, X. Zhang, B. Grabowski, A. V. Shapeev, and A. V. Ruban,
Ab initio simulations of the surface free energy of TiN(001)
Physical Review B 103, 195428 (2021).
X. Zhang, S. V. Divinski, and B. Grabowski,
Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten
submitted (2023).
X. Zhang, B. Grabowski, F. Körmann, C. Freysoldt, and J. Neugebauer,
Accurate electronic free energies of the 3d,4d, and 5d transition metals at high temperatures
Physical Review B 95, 165126 (2017).
X. Xu, X. Zhang, A. Ruban, S. Schmauder, and B. Grabowski,
Strong impact of spin fluctuations on the antiphase boundaries of weak itinerant ferromagnetic Ni3Al
Acta Materialia 255, 118986 (2023).
X. Zhang, S. V. Divinski, and B. Grabowski,
Ab initio prediction of vacancy energetics in HCP Al-Hf-Sc-Ti-Zr high entropy alloys and the subsystems
Acta Materialia 227, 117677 (2022).
A. Dash, A. Paul, S. Sen, S. Divinski, J. Kundin, I. Steinbach, B. Grabowski, and X. Zhang,
Recent Advances in Understanding Diffusion in Multiprincipal Element Systems
Annual Review of Materials Research 52, 383–409 (2022).
S. Sen, X. Zhang, L. Rogal, G. Wilde, B. Grabowski, and S. V. Divinski,
‘Anti-sluggish’ Ti diffusion in HCP high-entropy alloys: Chemical complexity vs. lattice distortions
Scripta Materialia 224, 115117 (2023).
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