Related papers: FerroAI: A Deep Learning Model for Predicting Phas…
We present a first-principles study of model domain walls (DWs) in prototypic ferroelectric PbTiO3. At high temperature the DW structure is somewhat trivial, with atoms occupying high- symmetry positions. However, upon cooling the DW…
We introduce an open-source, fully atomistic second-principles interatomic potential for lead titanate (PbTiO3), a benchmark ferroelectric material known for its strong polarization and hightemperature phase transitions. While density…
A single crystal of lead-zirconate-titanate (PZT), composition Pb(Zr0.80Ti0.20)O3, was studied by polarized-Raman scattering as a function of temperature. Raman spectra reveal that the local structure deviates from the average structure in…
Fundamental differences between materials originate from the unique nature of their constituent chemical elements. Before specific differences emerge according to the precise ratios of elements in a given crystal structure, a material can…
A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, methods are developed for generating contextual features and…
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase…
Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. However, the growing scale of simulations…
Pourbaix diagrams have long been an essential tool for determining the phase stability of solids and their associated ionic species under electrochemical conditions. In recent years, Pourbaix diagrams have been used for applications ranging…
We analyze a system of polar molecules in a one-dimensional optical lattice. By controlling the internal structure of the polar molecules with static electric and microwave fields, we demonstrate the appearance of a quantum phase transition…
Crystal structure prediction (CSP) stands as a powerful tool in materials science, driving the discovery and design of innovative materials. However, existing CSP methods heavily rely on formation enthalpies derived from density functional…
By assuming a more realistic nucleation and polarization reversal scenario we build a new statistical switching model for ferroelectrics, which is different from either the Kolmogorov-Avrami-Ishibashi (KAI) model or the…
The switching polarization of a ferroelectric is a characterization of the current that flows due to changes in polarization when the system is switched between two states. Computation of this change in polarization in crystal systems has…
Recently discovered fine structure of the morphotropic phase boundaries in highly piezoelectric mixture compounds PZT, PMN-PT, and PZN-PT demonstrates the importance of highly non-linear interactions in these systems. We show that an…
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems…
In this work, we first perform a systematic search for high-efficiency three-dimensional (3D) and two-dimensional (2D) thermoelectric materials by combining semiclassical transport techniques with density functional theory (DFT)…
We carry out a completely first-principles study of the ferroelectric phase transitions in BaTiO$_3$. Our approach takes advantage of two features of these transitions: the structural changes are small, and only low-energy distortions are…
Field Programmable Gate Array (FPGA) is widely used in acceleration of deep learning applications because of its reconfigurability, flexibility, and fast time-to-market. However, conventional FPGA suffers from the tradeoff between chip area…
Temperature-induced phase transition in BaTiO3 has been explored using the machine learning analysis of domain morphologies visualized via variable-temperature scanning transmission electron microscopy (STEM) imaging data. This approach is…
Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk…
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…