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High-throughput ab-initio dilute solute diffusion database.

Henry Wu¹, Tam Mayeshiba², Dane Morgan¹

¹Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.

Scientific Data

|July 20, 2016

Summary

This summary is machine-generated.

We automated the creation of diffusion databases using density functional theory (DFT) calculations. This work provides the largest collection of ab-initio solute diffusion data, showing good agreement with experimental results.

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Area of Science:

Materials Science
Computational Materials Science
Physical Chemistry

Background:

Understanding solute diffusion in alloys is crucial for predicting material properties and performance.
High-throughput computational methods offer a scalable approach to generating diffusion data.

Purpose of the Study:

To demonstrate the automated generation of diffusion databases using high-throughput density functional theory (DFT) calculations.
To compile the largest collection of consistently calculated ab-initio solute diffusion data.
To validate the accuracy of the calculated diffusion data against experimental results.

Main Methods:

Utilized high-throughput density functional theory (DFT) calculations for automated database generation.
Employed multi-frequency diffusion models to determine over 230 dilute solute diffusion systems.
Applied a correction method for solute diffusion using host self-diffusivity data.

Main Results:

Generated a database of over 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt.
Achieved good agreement with experimental solute diffusion data.
Obtained a weighted activation barrier RMS error of 0.176 eV (excluding magnetic solutes).

Conclusions:

Automated DFT calculations provide a powerful tool for generating large-scale, accurate diffusion databases.
The compiled database represents a significant resource for materials science research.
The findings support the predictive capability of ab-initio methods for solute diffusion in alloys.