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Tuning Oxide Properties by Oxygen Vacancy Control During Growth and Annealing

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Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide.

Ganesh Sivaraman¹, Leighanne Gallington², Anand Narayanan Krishnamoorthy³

¹Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439, USA.

Physical Review Letters

|April 30, 2021

Summary

This study developed an automated machine learning approach to efficiently model refractory oxides like Hafnium Dioxide (HfO2) across a wide temperature range. This accelerates materials discovery for high-temperature applications.

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Last Updated: Nov 7, 2025

Tuning Oxide Properties by Oxygen Vacancy Control During Growth and Annealing

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Published on: June 9, 2023

Fabrication of Spatially Confined Complex Oxides

Fabrication of Spatially Confined Complex Oxides

Published on: July 1, 2013

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

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Published on: May 29, 2018

Area of Science:

Materials Science
Computational Materials Science
Thermodynamics

Background:

Refractory oxides are crucial for high-temperature applications.
Understanding their structure-property relationships is essential but challenging.
Existing methods for modeling these materials can be time-consuming.

Purpose of the Study:

To develop an automated, efficient method for modeling refractory oxides.
To accelerate the discovery and development of new high-temperature materials.
To create a robust machine learning potential for Hafnium Dioxide (HfO2).

Main Methods:

Combined experimental (X-ray and neutron diffraction) and simulation approach.
Utilized an active learning framework with an automated closed loop.
Generated a multiphase potential for HfO2 from room temperature to the liquid state (~2900°C).

Main Results:

Successfully developed a machine learning model covering the phase space of HfO2.
The automated approach significantly reduced model development time.
Minimized human effort required for creating accurate material models.

Conclusions:

The active learning-driven approach is highly effective for modeling refractory oxides.
This method accelerates the generation of accurate interatomic potentials.
Enables faster exploration of material properties for demanding applications.