Related papers: Machine learning Landau free energy potentials
In this paper we explain how to set up what is in fact the only possible consistent construction scheme for a Landau theory of high pressure phase transitions that systematically allows to take into account elastic nonlinearities. We also…
Based on the locality principle of insulating superlattices, we apply the method of Wu {\it et al} [Phys. Rev. Letter {\bf 101}, 087610 (2008)] to the piezoelectric strains of individual layers under fixed displacement field. For a…
Since electronic and magnetic properties of many transition-metal oxides can be efficiently controlled by external factors such as the temperature, pressure, electric or magnetic field, they are regarded as promising materials for various…
There is a general perception that large piezoelectric response in ferroelectric alloys requires tuning the system towards a morphotropic phase boundary (MPB), i.e., a composition driven inter-ferroelectric instability. Here we show that…
Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging…
Ferroelectric materials with switchable spontaneous polarization underpin non-volatile memories, transistors, sensors, and emerging neuromorphic chips. Their performance and stability are governed by polarization dynamics and domain…
Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase…
In the first-principles bulk-layer model the superlattice structure and polarization are determined by first-principles computation of the bulk responses of the constituents to the electrical and mechanical boundary conditions in an…
Materials properties depend strongly on chemical composition, i.e., the relative amounts of each chemical element. Changes in composition lead to entirely different chemical arrangements, which vary in complexity from perfectly ordered…
In the development of first-principles high-throughput searches for materials with desirable functional properties, there is a clear need for an efficient method to determine the ground state and low-energy alternative structures of…
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin orientations and…
This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth…
The formation of a two-dimensional electron gas at oxide interfaces as a consequence of polar discontinuities has generated an enormous amount of activity due to the variety of interesting effects it gives rise to. Here we study under what…
Data-driven, machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements into predictions of energies and forces. As a result, these…
We have fabricated a variety of "PZT-PFW" (PbZr0.52Ti0.48O3)1-x(PbFe2/3W1/3O3)x [PZTFWx; 0.2 < x < 0.4] single-phase tetragonal ferroelectrics via chemical solution deposition (CSD) [polycrystalline] and pulsed laser deposition (PLD)…
Uranium dioxide (UO2) is a prototypical nuclear fuel material, yet predicting its thermophysical properties across a wide temperature range remains challenging. One factor contributing to this difficulty is the complex magnetic ordering at…
The behavior of PbTiO$_3$ under uniaxial strains and stresses is investigated from first-principles calculations within density functional theory. We show that irrespectively of the uniaxial mechanical constraint applied, the system keeps a…
Proximity to phase transitions (PTs) is frequently responsible for the largest dielectric susceptibilities in ferroelectrics. The impracticality of using temperature as a control parameter to reach those large responses has motivated the…
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial…
Ab initio calculations are utilized as an input to develop a simple model of polarization in epitaxial short-period CaTiO3/SrTiO3/BaTiO3 superlattices grown on a SrTiO3 substrate. The model is then combined with a genetic algorithm…