DOE OSTI · 3020131
Extending SLUSCHI for Automated Diffusion Calculations
Abstract
We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid-liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self-diffusion in Al-Cu liquid alloys, sublattice melting in Li7La3Zr2O12 and Er2O3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe-O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes-Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides.
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Hong, Qi-Jun [Arizona State University], Chen, Qing [Royal Institute of Technology; Thermo-Calc Software AB], Wang, Ligen [Arizona State University], Fisher, Dallin [Arizona State University], Campbell, Audrey [Arizona State University], Sue, Si-Da [Arizona State University], Mu, Linqin [Arizona State University], Leick, Noemi [National Laboratory of the Rockies, Golden, CO (United States)], Seetharaman, Sridhar [Arizona State University]. 2026-02-09. Extending SLUSCHI for Automated Diffusion Calculations. https://doi.org/10.1016/j.calphad.2026.102923
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