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Ab initio simulations of the surface free energy of TiN(001)

, , , , and . Physical review. B, Condensed matter and materials physics, 103 (19): 195428 (2021)
DOI: 10.1103/PhysRevB.103.195428

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High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, , , and . npj computational materials, (2023)Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, , , and . Dataset, (2023)Related to: A. Forslund, J. H. Jung, P. Srinivasan, and B. Grabowski, Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). doi: 10.1103/PhysRevB.107.174309.Temperature dependence of (111) and (110) ceria surface energy, , , , and . Physical review. B, Condensed matter and materials physics, 107 (3): 035407 (2023)Ab initio simulations of the surface free energy of TiN(001), , , , and . Physical review. B, Condensed matter and materials physics, 103 (19): 195428 (2021)Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, , , and . Dataset, (2023)Related to: J. H. Jung, A. Forslund, P. Srinivasan, and B. Grabowski, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). doi: 10.1103/PhysRevB.108.184107.Low-pressure CVD of (Tix,W1-x)Ny from WF6, TiCl4 and NH3, , , , , and . Surface and coatings technology, (2022)Thermodynamic properties on the homologous temperature scale from direct upsampling : Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, , , and . Physical review. B, Condensed matter and materials physics, 107 (17): 174309 (2023)Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, , , and . Dataset, (2023)Related to: J. H. Jung, P. Srinivasan, A. Forslund, and B. Grabowski, "High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", npj Comput. Mater. 9, 3 (2023). doi: 10.1038/s41524-022-00956-8.