A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia
Sebastian Bichelmaier, Jes\'us Carrete, Ralf Wanzenb\"ock, Florian, Buchner, Georg K. H. Madsen

TL;DR
This study uses a neural-network-based effective harmonic potential to efficiently explore the complex phase structure of hafnia, achieving near ab-initio accuracy and revealing insights into phase stability and thermal properties.
Contribution
The paper introduces a neural-network force field approach for studying hafnia's phases, enabling accurate, cost-effective phase analysis and phase stability predictions.
Findings
The neural-network force field generalizes well across hafnia phases.
The P-43m phase is identified as the stable cubic phase.
Stoichiometric cubic phases are likely metastable or defect-stabilized.
Abstract
Phonon-based approaches and molecular dynamics are widely established methods for gaining access to a temperature-dependent description of material properties. However, when a compound's phase space is vast, density-functional-theory-backed studies quickly reach prohibitive levels of computational expense. Here, we explore the complex phase structure of HfO2 using effective harmonic potentials based on a neural-network force field (NNFF) as a surrogate model. We detail the data acquisition and training strategy that enable the NNFF to provide almost ab-initio accuracy at a significantly reduced cost and present a recipe for automation. We demonstrate how the NNFF can generalize beyond its training data and that it is transferable between several phases of hafnia. We find that the thermal expansion of the low-symmetry phases agrees well with experimental results and we determine the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
