Extending SLUSCHI for Automated Diffusion Calculations

TL;DR

The paper tackles the challenge of obtaining first-principles diffusion and viscosity data for liquids and sublattice-melting solids. It extends the SLUSCHI workflow to isolate the volume-search stage, run a single production AIMD trajectory, and automatically post-process VASP outputs to extract self-diffusion coefficients via the Einstein relation, with MSD fits and block-averaged uncertainties. The approach is validated across diverse systems, including liquid Al–Cu alloys, LLZO Li sublattice melting, Er$_2$O$_3$ oxygen diffusion, and O transport in Fe/FeO–SiO$_2$–Al$_2$O$_3$, demonstrating accurate trends and compatibility with experimental/CALPHAD data, while linking diffusivity to viscosity through the Stokes–Einstein relation with linear composition mixing. This automation enables high-throughput, fully first-principles transport datasets for metals and oxides, supporting materials design and fundamental understanding of high-temperature transport phenomena; future work includes ensemble averaging, Green–Kubo viscosity, and path-integral or active-learning potentials to extend timescales and accuracy.

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- and inter-diffusion in Al-Cu liquid alloys, sublattice melting in Li_7La_3Zr_2O_12 and Er_2O_3, 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.

Figures (7)

• Figure 1: Workflow of the SLUSCHI framework. The original modules (optunitcell, volsearch, meltcoex, and coexrun) are shown schematically to highlight their sequence from unit-cell optimization and supercell construction through thermal expansion, solid–liquid coexistence, and melting-point fitting. For diffusion calculations, only the first two stages (unit-cell optimization and volume search/thermal expansion) are executed, generating one long ab initio molecular dynamics trajectory of equilibrated liquid or partially disordered configurations for subsequent automated diffusion analysis. This modular design allows the same infrastructure to perform both melting and high-temperature transport calculations within a unified, fully automated DFT–MD workflow.
• Figure 2: Viscosity (scaled by a factor of 2 due to PBE underbinding nature) of Al–Cu liquids at various compositions at 1173, 1500, and 2000 K, compared with experimental data Schick2012 and CALPHAD calculations using the TCAL9 thermodynamic database TCAL9_2025.
• Figure 3: Diffusion coefficient of lithium in LLZO from 600 to 1400 K. Sublattice melting occurs between 800 and 1000 K.
• Figure 4: Lattice constants $a$, $b$, and $c$ in LLZO at 600 K (balck trace) and 1400 K (red trace).
• Figure 5: Diffusion coefficients in LLZO from 600 to 1400 K.