Modular development of deep potential for complex solid solutions

Abstract

The multicomponent oxide solid solution is a versatile platform to tune the delicate balance between competing spin, charge, orbital, and lattice degrees of freedom for materials design and discovery. The development of compositionally complex oxides with superior functional properties has been largely empirical and serendipitous, in part due to the exceedingly complex chemistry and structure of solid solutions that span a range of length scales. The classical molecular dynamics (MD), as a powerful statistical method to investigate materials properties over large spatial and temporal scales, often plays a secondary role in computer-aided materials discovery because of the limited availability and accuracy of classical force fields. Here, we introduce the strategy of ``modular developing deep potential" (ModDP) that enables a systematic development and improvement of deep neural network-based model potential, termed as deep potential, for complex solid solutions with minimum human intervention. The converged training database associated with an end-member material is treated as an independent module and is reused to train the deep potential of solid solutions via a concurrent learning procedure. We apply ModDP to obtain classical force fields of two technologically important solid solutions, Pb${}_x$Sr${}_{1-x}$TiO${}_3$ and Hf${}_x$Zr${}_{1-x}$O${}_2$. For both materials systems, a single model potential is capable of predicting various properties of solid solutions including temperature-driven and composition-driven phase transitions over a wide range of compositions. In particular, the deep potential of Pb${}_x$Sr${}_{1-x}$TiO${}_3$ reproduces a few known topological textures such as polar vortex lattice and electric dipole waves in PbTiO${}_3$/SrTiO${}_3$ superlattices, paving the way for MD investigations on the dynamics of topological structures in response to external stimuli.

32 pages, 9 figures

References in corpus (9)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Pathways Towards Ferroelectricity in Hafnia
• Structural Polymorphism Kinetics Promoted by Charged Oxygen Vacancies in HfO${}_2$
• Deep Learning of Accurate Force Field of Ferroelectric HfO${}_2$
• Structural Phase Transitions in SrTiO3 from Deep Potential Molecular Dynamics
• Large-scale Atomistic Simulation of Quantum Effects in SrTiO${}_3$ from First Principles
• Molecular Dynamics Simulations of Chemically Disordered Ferroelectric (Ba,Sr)TiO${}_3$ with a Semi-Empirical Effective Hamiltonian
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Cited by in corpus (10)

• Progress in Computational Understanding of Ferroelectric Mechanisms in HfO${}_2$
• Ultrahigh oxygen ion mobility in ferroelectric hafnia
• Universal interatomic potential for perovskite oxides
• Giant piezoelectric effects of topological structures in stretched ferroelectric membranes
• Finite-temperature properties of antiferroelectric perovskite $\mathrm{PbZr}{\mathrm{O}}_3$ from deep learning interatomic potential
• Origin of Interstitial Doping Induced Coercive Field Reduction in Ferroelectric Hafnia
• Theoretical lower limit of coercive field in ferroelectric hafnia
• Assessing the nature of nanoscale ferroelectric domain walls in lead titanate multilayers
• Topological phase transitions in perovskite superlattices driven by temperature, electric field, and doping
• Ferroelectric Properties and Topological Polar Textures of PbTiO${}_3$ from a Second-Principles Open-Source Interatomic Potential