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Composites of ferroelectric and paraelectric perovskites have attracted a lot of attention due to their application potential in energy storage as well as novel computing and memory devices. So far the main focus of research has been on…
New experimental data on solid solutions of quantum paraelectrics with KTaO_3:Nb as an example are considered within a framework of a quantum theory of ferroelectric phase transitions. In order to describe the effect of local…
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage pre-defined structural…
Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning…
Nanoscale ferroelectric topologies such as vortices, anti-vortices, bubble patterns etc. are stabilized in thin films by a delicate balance of both mechanical and electrical boundary conditions. A systematic understanding of the phase…
The switching characteristics of ferroelectrics and multiferroics are influenced by the interaction of topological defects with domain-walls. We report on the pinning of polarization due to antiphase boundaries in thin films of the…
Leveraging scanning tunneling microscopy (STM) for atomic-scale fabrication has led to many advancements such as the creation of atomic electron-spin qubit structures on surfaces. However, the time-consuming and tedious nature of this…
Multiferroic oxides, such as BiFeO3, have garnered significant attention due to their coupled ferroelectric, magnetic, and elastic properties, offering exciting opportunities for multifunctional device applications. Controlling phase…
An effective Hamiltonian for the ferroelectric transition in $PbTiO_3$ is constructed from first-principles density-functional-theory total-energy and linear-response calculations through the use of a localized, symmetrized basis set of…
This study addresses microstructure selection mechanisms in rapid solidification, specifically targeting the transition from cellular/dendritic to planar interface morphologies under conditions relevant to additive manufacturing. We use a…
Phase-field simulation (PFS) have revolutionized the understanding of domain structure and switching behavior in ferroelectric thin films and ceramics. Generally, PFS is based on solution of a (set) of Ginzburg-Landau equations for a…
The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that…
Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and…
The lattice instabilities of perovskite structure oxides are responsible for many of their interesting properties, such as temperature-dependent ferroelectric phase transitions. First-principles calculations using linear response theory…
The breaking of ergodicity in isolated quantum systems with a single-particle mobility edge is an intriguing subject that has not yet been fully understood. In particular, whether a nonergodic but metallic phase exists or not in the…
Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is…
Analytical description of domain structure morphology and phase diagrams of ferroelectric nanoparticles is developed in the framework of Landau Ginzburg Devonshire approach. To model realistic conditions of incomplete screening of…
Meshfree particle methods, such as Smoothed Particle Hydrodynamics (SPH) and the Moving Particle Semi-Implicit (MPS) method, are widely used to simulate complex free-surface and multiphase flows. A key challenge in these methods is the…
We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks---a class of…
Lead halide perovskites (APbX$_3$) offer tunable optoelectronic properties but feature an intricate phase-stability landscape. Here we employ on-the-fly data collection and an equivariant message-passing neural-network potential to perform…