A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia

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 P-43m phase to be the favorable (stoichiometric) cubic phase over the established Fm-3m. In contrast, the experimental lattice constants of the cubic phases are substantially larger than what is calculated for the corresponding stoichiometric phases. Furthermore, we show that the stoichiometric cubic phases are unlikely to be thermodynamically stable compared to the tetragonal and monoclinic phases, and hypothesize that they only exist in defect-stabilized forms.

References in corpus (23)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• A More Accurate Generalized Gradient Approximation for Solids
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• Phase transitions in BaTiO${}_3$ from first principles
• Temperature dependent effective potential method for accurate free energy calculations of solids
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• Pathways Towards Ferroelectricity in Hafnia
• Machine learning a general purpose interatomic potential for silicon
• Anharmonic free energies and phonon dispersions from the stochastic self-consistent harmonic approximation: application to platinum and palladium hydrides
• Strain controlled oxygen vacancy formation and ordering in CaMnO${}_3$
• First-Principles Theory of Anharmonicity and the Inverse Isotope Effect in Superconducting Palladium-Hydride Compounds
• Rungs 1 to 4 of DFT Jacob's ladder: extensive test on the lattice constant, bulk modulus, and cohesive energy of solids
• The Stochastic Self-Consistent Harmonic Approximation: Calculating Vibrational Properties of Materials with Full Quantum and Anharmonic Effects
• Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
• Comparing the Weighted Density Approximation with the LDA and GGA for Ground State Properties of Ferroelectric Perovskites
• Deep Learning of Accurate Force Field of Ferroelectric HfO${}_2$
• Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
• Structural Phase Transitions in SrTiO3 from Deep Potential Molecular Dynamics
• High throughput thermal conductivity of high temperature solid phases: The case of oxide and fluoride perovskites
• An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
• Quantum Self-Consistent Ab-Initio Lattice Dynamics
• Magnetic properties of HO2 thin films

Cited by in corpus (1)

• Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling