Journal
PHYSICAL REVIEW B
Volume 103, Issue 19, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.103.195428
Keywords
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Funding
- Swedish Foundation for Strategic Research via SSF [RMA15-0048]
- European Research Council (ERC) under the EU's Horizon 2020 Research and Innovation Programme [865855]
- DFG-RFBR Grant [DFG KO 5080/3-1, DFG GR 3716/6-1, RFBR 20-53-12012]
- Stuttgart Center for Simulation Science (SimTech)
- European Research Council
- Swedish Governmental Agency for Innovation Systems (VINNOVA), Swedish industry
- Royal Institute of Technology (KTH)
- COMET program within the K2 Center Integrated Computational Material, Process and Product Engineering (IC-MPPE) [859480]
- Austrian Federal Ministries for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK)
- Austrian research funding association (FFG)
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The study investigated the temperature dependence of TiN(001) surface free energy using a new thermodynamic method and found that anharmonic vibrations have a significant impact on the surface free energy. The use of machine-learning potentials provided surface free energy results close to those obtained from full AIMD simulations.
The temperature dependence of the surface free energy of the industrially important TiN(001) system has been investigated by means of an extended two-stage upsampled thermodynamic integration using Langevin dynamics (TU-TILD) methodology, to include the fully anharmonic vibrational contribution, as obtained from ab initio molecular dynamics (AIMD). Inclusion of the fully anharmonic behavior is crucial, since the standard low-temperature quasiharmonic approximation exhibits a severe divergence in the surface free energy due to a high-temperature dynamical instability. The anharmonic vibrations compensate for the quasiharmonic divergence and lead to a modest overall temperature effect on the TiN(001) surface free energy, changing it from around 78 meV angstrom(-2) at 0 K to 73 meV angstrom(-2) at 3000 K. The statistical convergence of the molecular dynamics is facilitated by the use of machine-learning potentials, specifically moment tensor potentials, fitted for TiN(001) at finite temperature. The surface free energy obtained directly from the fitted machine-learning potentials is close to that obtained from the full AIMD simulations.
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