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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ferroelectric materials toward next-generation electromechanical technologies

Ferroelectric materials have been widely used in various electromechanical devices, from ultrasonic transducers and actuators to mechanical energy harvesters. The key performance metrics of these devices, such as sensitivity, efficiency, and bandwidth of ultrasonic transducers, are largely determined by the piezoelectric properties. This Review highlights recent research progress in improving the piezoelectricity of ferroelectric materials and offers potential strategies for further enhancement to meet the ever-increasing demands for high-performance piezoelectric devices and systems. Here, it provides insights into the future development of ferroelectrics to address the increasing demands of emerging applications, including photoacoustic imaging and piezoelectric fans and motors in integrated circuit–enabled electronic devices. Additionally, it emphasizes the need to consider environmental impacts across the entire life cycle of ferroelectrics, from sourcing and manufacturing to usage and disposal.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optical subpicosecond nonvolatile switching and electron-phonon coupling in ferroelectric materials

Direct optical ferroelectric switching (FE switch) has the advantage of being ultrafast over the traditional FE switch, which relies on domain nucleation and growth. However, how to realize nonvolatility in such an optical FE switch poses a serious challenge. Time-dependent density-functional theory molecular dynamics study reveals that subpicosecond nonvolatile FE switches can be realized in GeTe and PbTiO 3 . Here, while optical tuning of the transition barrier and initiation of a directional atomic motion are crucial, the dephasing of the excited state holds the key for the realization of nonvolatility.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dislocation-induced large local polarization inhomogeneity of ferroelectric materials

Dislocations in ferroelectrics play important roles in ferroelectricity, piezoelectricity, and dielectricity. However, the intrinsic charge-lattice coupling mechanism in ferroelectric crystals containing dislocations is still not well understood. Here, we report a large local polarization inhomogeneity of ~100 μCcm –2 induced by a single a[001] dislocation in the PbZr 0.2 Ti 0.8 O 3 /SrTiO 3 film/substrate heterostructure, the tensile region (~120 μCcm –2 ) and the compressive strain region (~21 μCcm –2 ) around the dislocation forms a butterfly-like area. Furthermore, this study reveals the dramatic effects of dislocations on local polarization and provides a strategy to manipulate the polarization magnitude and orientation of local polarization by defect engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chapter 3 - Harvesting Thermal Energy with Ferroelectric Materials

Thermal energy is considered the ubiquitous form of energy as all other forms of energy ultimately degrade to thermal energy. Thermal to electrical energy conversion is currently an important method for electricity generation. The traditional power cycles, however, become technically and economically unviable when heat source temperature reduces below 100 °C. Thermal energy harvesting has been proposed as a viable alternative for low-grade thermal to electrical energy conversion. Thermal energy harvesting is a method of generating electricity using temperature-induced variations in material properties. It is particularly useful when the heat sources are available free-of-cost, either in the form of natural heat or waste heat. While there are several thermal energy harvesting techniques, pyroelectricity is the most promising as it can be used in situations where spatial thermal gradients are difficult to establish or the temperature of the heat source is changing. Despite the fact that the pyroelectric effect in ferroelectric materials has been known for past several decades, thermal energy harvesting using ferroelectric materials has not been much explored in the literature. In this chapter, we would explore various aspects of thermal energy harvesting using ferroelectric materials. We have first established the physics behind ferroelectricity, then provided a detailed description on ferroelectric thermal energy harvesting, discussed various ferroelectric thermodynamic cycles and devices proposed in the literature, and finally described various other emerging applications of ferroelectricity.

DIRECT ENERGY CONVERSION,MATERIALS SCIENCE,OTHER I↗
DOE OSTI2020

Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials

In this work, imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Symmetry relation database and its application to ferroelectric materials discovery

To investigate the displacive phase transition at the atomic scale, we have implemented a numerical algorithm to automate the detection of the symmetry relations between any two candidate crystal structures. Using this algorithm, here we systematically screen all possible polar–nonpolar structure pairs from the Materials Project database and establish a library of ~4500 pairs that can be connected through a continuous phase transition with small atomic displacements. From this database, we identify several new ferroelectric materials. In addition, the database may also be used in other areas, such as material structure prediction and new materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Q-POP-Thermo: A general-purpose thermodynamics solver for ferroelectric materials

We report that Q-POP-Thermo is a program designed to compute thermodynamic monodomain equilibrium states and their properties for ferroelectric single crystals and thin films based on the Landau-Ginzburg-Devonshire (LGD) Theory. Utilizing symbolic manipulation with the SymPy Library, the governing equations along with appropriate boundary conditions are solved for speedy minimization of the free energy of a crystal. Utilizing the popular Differential Evolution algorithm, with appropriate hybridization, multiple phase diagrams, such as the pressure-temperature phase diagram for bulk single crystals and the common strain-temperature phase diagram for monodomain thin-film systems can be readily generated. Furthermore, a variety of material properties of stable ferroelectric phases, including dielectric, piezoelectric, and electrocaloric properties, can simultaneously be calculated. Validation studies are presented for both thin-film and single crystal systems to test the effectiveness and capability of the open-source program.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Automated Experiments of Local Non–Linear Behavior in Ferroelectric Materials

An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al 0.93 B 0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al 0.93 B 0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. Furthermore, this technique can also be extended to electron, probe, and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Candidate ferroelectrics via ab initio high-throughput screening of polar materials

Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors.

36 MATERIALS SCIENCE↗