Dielectric capacitors can store and release electric energy at ultrafast rates and are extensively studied for applications in electronics and electric power systems Among various candidates, thin films based on relaxor ferroelectrics, a special kind of ferroelectric with nanometer-sized domains, have attracted special attention because of their high energy densities and efficiencies We show that high-energy ion bombardment improves the energy storage performance of relaxor ferroelectric thin films Intrinsic point defects created by ion bombardment reduce leakage, delay low-field polarization saturation, enhance high-field polarizability, and improve breakdown strength We demonstrate energy storage densities as high as ~133 joules per cubic centimeter with efficiencies exceeding 75% Deterministic control of defects by means of postsynthesis processing methods such as ion bombardment can be used to overcome the trade-off between high polarizability and breakdown strength

/pdf/ultrahigh-capacitive-energy-density-in-ion-bombarded-relaxor-3awym1bmvl.pdf

Ultrahigh capacitive energy density in ion-bombarded relaxor ferroelectric films

Since the positive influences of defects on the performance of electroceramics were discovered, investigations concerning on defects and aliovalent doping routes have grown rapidly in the fields of inorganic chemistry and condensed matter physics. In this article, we summarized the types of defects in electroceramics as well as characterization tools of defects and highlighted the effects of intrinsic and extrinsic defects on the material performances with the emphasis on dielectric, ferroelectric, and piezoelectric properties. We mainly introduced defect related theoretical simulation and experimental results in several typical incipient ferroelectrics, ferroelectrics, and antiferroelectrics. Hence, the influences of defects on the crystal lattice were summed up, and then the main physical mechanisms were highlighted. Particularly, the performance enhancements of aliovalently doped electroceramics were also evaluated and reviewed. Finally, the outlook and challenges were discussed on the basis of their current developments. This article covers not only an overview of the state-of-the-art advances of defects and aliovalent doping routes in electroceramics but also the future prospects that may open another window to tune the electrical performance of electroceramics via intentionally introducing certain defects.

Defects and Aliovalent Doping Engineering in Electroceramics.

The properties of ferroelectric materials, such as lead zirconate titanate (PZT), are heavily influenced by the interaction of defects with domain walls. These defects are either intrinsic, or are induced by the addition of dopants. We study here PbTiO$_3$ (the end member of a key family of solid solutions) in the presence of acceptor (Fe) and donor (Nb) dopants, and the interactions of the different defects and defect associates with the domain walls. For the case iron acceptors, the calculations point to the formation of defect associates involving an iron substitutional defect and a charged oxygen vacancy (Fe$^{'}_{Ti}$-V$^{^{\textbf{..}}}_O$). This associate exhibits a strong tendency to align in the direction of the bulk polarization; in fact, ordering of defects is also observed in pure PbTiO$_3$ in the form of lead-oxygen divacancies. Conversely, calculations on donor-doped PbTiO$_3$ do not indicate the formation of polar defect complexes involving donor substitutions. Last, it is observed that both isolated defects in donor-doped materials and defect associates in acceptor-doped materials are more stable at 180$^o$ domain walls. However, polar defect complexes lead to asymmetric potentials at domain walls due to the interaction of the defect polarization with the bulk polarization. The relative pinning characteristics of different defects are then compared, to develop an understanding of defect-domain wall interactions in both doped and pure PbTiO$_3$. These results may also help understanding hardening and softening mechanisms in PZT.

Defect ordering and defect-domain wall interactions in PbTiO$_3$: A first-principles study

Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysical trajectories. Our character-level language model learns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensional dynamics. The model captures Boltzmann statistics and also reproduces kinetics across a spectrum of timescales. We demonstrate how training the long short-term memory network is equivalent to learning a path entropy, and that its embedding layer, instead of representing contextual meaning of characters, here exhibits a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate our model’s reliability through different benchmark systems and a force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of recurrent neural networks for understanding the dynamics of complex stochastic molecular systems. Artificial neural networks have been successfully used for language recognition. Tsai et al. use the same techniques to link between language processing and prediction of molecular trajectories and show capability to predict complex thermodynamics and kinetics arising in chemical or biological physics.

/pdf/learning-molecular-dynamics-with-simple-language-model-built-3b4ipnfpwl.pdf

Learning molecular dynamics with simple language model built upon long short-term memory neural network.

Polarization switching mechanisms in ferroelectric materials are fundamentally linked to local domain structure and the presence of the structural defects, which both can act as nucleation and pinn...

Toward Decoding the Relationship between Domain Structure and Functionality in Ferroelectrics via Hidden Latent Variables.

Electric-field switching of polarization is the building block of a wide variety of ferroelectric devices. In turn, understanding the factors affecting ferroelectric switching and developing routes to control it are of great technological significance. This work provides systematic experimental evidence of the role of defects in affecting ferroelectric-polarization switching and utilizes the ability to deterministically create and spatially locate point defects in $\mathrm{PbZ}{\mathrm{r}}_{0.2}\mathrm{T}{\mathrm{i}}_{0.8}{\mathrm{O}}_{3}$ thin films via focused-helium-ion bombardment and the subsequent defect-polarization coupling as a knob for on-demand control of ferroelectric switching (e.g., coercivity and imprint). At intermediate ion doses ($0.22--2.2\ifmmode\times\else\texttimes\fi{}{10}^{14}\phantom{\rule{0.16em}{0ex}}\mathrm{ions}\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}2}$), the dominant defects (isolated point defects and small clusters) show a weak interaction with domain walls (pinning potentials from $200--500\phantom{\rule{0.16em}{0ex}}\mathrm{K}\phantom{\rule{0.16em}{0ex}}\mathrm{MV}\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$), resulting in small and symmetric changes in the coercive field. At high doses ($0.22--1\ifmmode\times\else\texttimes\fi{}{10}^{15}\phantom{\rule{0.16em}{0ex}}\mathrm{ions}\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}2}$), on the other hand, the dominant defects (larger defect complexes and clusters) strongly pin domain-wall motion (pinning potentials from 500 to $1600\phantom{\rule{0.16em}{0ex}}\mathrm{K}\phantom{\rule{0.16em}{0ex}}\mathrm{MV}\phantom{\rule{0.16em}{0ex}}{\mathrm{cm}}^{\ensuremath{-}1}$), resulting in a large increase in the coercivity and imprint, and a reduction in the polarization. This local control of ferroelectric switching provides a route to produce novel functions; namely, tunable multiple polarization states, rewritable pre-determined 180\ifmmode^\circ\else\textdegree\fi{} domain patterns, and multiple zero-field piezoresponse and permittivity states. Such an approach opens up pathways to achieve multilevel data storage and logic, nonvolatile self-sensing shape-memory devices, and nonvolatile ferroelectric field-effect transistors.

https://link.aps.org/accepted/10.1103/PhysRevMaterials.2.084414

Local control of defects and switching properties in ferroelectric thin films

The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material's physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials.

/pdf/revealing-ferroelectric-switching-character-using-deep-1lqj0ooml0.pdf

Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures of ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.

/pdf/machine-detection-of-enhanced-electromechanical-energy-1rqqbes8bs.pdf

Joshua Maher

Papers

Local control of defects and switching properties in ferroelectric thin films

Revealing Ferroelectric Switching Character Using Deep Recurrent Neural Networks

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr0.2 Ti0.8 O3 Thin Films.