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Gorai, Prashun

Publications and source records attributed to Gorai, Prashun.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GdWN 3 is a nitride perovskite

Nitride perovskites ABN3 are an emerging and highly underexplored class of materials that are of interest due to their intriguing calculated ferroelectric, optoelectronic, and other functional properties. Incorporating novel A-site cations is one strategy to tune and expand such properties; for example, Gd 3+ is compelling due to its large magnetic moment, potentially leading to multiferroic behavior. However, the theoretically predicted ground state of GdWN 3 was a non-perovskite monoclinic structure. Here, we experimentally show that GdWN 3-y crystallizes in a perovskite structure. High-throughput combinatorial sputtering with activated nitrogen is employed to synthesize thin films of Gd 2-x W x N 3-y O y with oxygen content y < 0.05. Ex situ annealing crystallizes a polycrystalline perovskite phase in a narrow composition window near x = 1. LeBail fits of synchrotron grazing incidence wide angle x-ray scattering data are consistent with a perovskite ground-state structure. Refined density functional theory calculations that included antiferromagnetic configurations confirm that the ground-state structure of GdWN 3 is a distorted Pnma perovskite with antiferromagnetic ordering, in contrast to prior predictions. Initial property measurements find that GdWN 3-y is paramagnetic down to T = 2 K with antiferromagnetic correlations and that the absorption onset depends on cation stoichiometry. This work provides an important path toward both the rapid expansion of the emerging family of nitride perovskites and understanding their potential multiferroic properties.

14 SOLAR ENERGY↗
DOE OSTI2024

Switching it up: New mechanisms revealed in wurtzite-type ferroelectrics

Wurtzite-type ferroelectrics have drawn increasing attention due to the promise of better performance and integration than traditional oxide ferroelectrics with semiconductors such as Si, SiC, and III-V compounds. However, wurtzite-type ferroelectrics generally require enormous electric fields, approaching breakdown, to reverse their polarization. The underlying switching mechanism(s), especially for multinary compounds and alloys, remains elusive. Here, we examine the switching behaviors in Al 1-x Sc x N alloys and wurtzite-type multinary candidate compounds we recently computationally identified. We find that switching in these tetrahedrally coordinated materials proceeds via a variety of nonpolar intermediate structures and that switching barriers are dominated by the more-electronegative cations. For Al 1-x Sc x N alloys, we find that the switching pathway changes from a collective mechanism to a lower-barrier mechanism enabled by inversion of individual tetrahedra with increased Sc composition. Our findings provide insights for future engineering and realization of wurtzite-type materials and open a door to understanding domain motion.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Defect control strategies for Al 1- x Gd x N alloys

Tetrahedrally bonded III-N and related alloys are useful for a wide range of applications from optoelectronics to dielectric electromechanics. Heterostructural AlN-based alloys offer unique properties for piezoelectrics, ferroelectrics, and other emerging applications. Atomic-scale point defects and impurities can strongly affect the functional properties of materials, and therefore, it is crucial to understand the nature of these defects and the mechanisms through which their concentrations may be controlled in AlN-based alloys. In this study, we employ density functional theory with alloy modeling and point defect calculations to investigate native point defects and unintentional impurities in Al 1-x Gd x N alloys. Among the native defects that introduce deep midgap states, nitrogen vacancies (V N ⁠) are predicted to be in the highest concentration, especially under N-poor growth conditions. We predict and experimentally demonstrate that V N formation can be suppressed in thin films through growth in N-rich environments. We also find that Al 1-x Gd x N alloys are prone to high levels of unintentional O incorporation, which indirectly leads to even higher concentrations of deep defects. Growth under N-rich/reducing conditions is predicted to minimize and partially alleviate the effects of O incorporation. The results of this study provide valuable insights into the defect behavior in wurtzite nitride-based alloys, which can guide their design and optimization for various applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2022

AlScO 3 perovskite—An ~8 eV bandgap oxide predicted to exhibit low small hole polaron ionization energies and p -type conductivity at elevated temperatures

Here, we investigate electronic structure and dopability of an ultrawide bandgap (UWBG) AlScO 3 perovskite, a known high-pressure and long-lived metastable oxide. From first-principles electronic structure calculations, HSE06(+G 0 W 0 ), we find this material to exhibit an indirect bandgap of around 8.0 eV. Defect calculations point to cation and oxygen vacancies as the dominant intrinsic point defects limiting extrinsic doping. While acceptor behaving Al and Sc vacancies prevent n-type doping, oxygen vacancies permit the Fermi energy to reach ~0.3 eV above the valence band maximum, rendering AlScO 3 p-type dopable. Furthermore, we find that both Mg and Zn could serve as extrinsic p-type dopants. Specifically, Mg is predicted to have achievable net acceptor concentrations of ~10 17 cm -3 with ionization energy of bound small hole polarons of ~0.49 eV and free ones below 0.1 eV. These values place AlScO 3 among the UWBG oxides with lowest bound small hole polaron ionization energies, which, as we find, is likely due to large ionic dielectric constant that correlates well with low hole polaron ionization energies across various UWBG oxides.

14 SOLAR ENERGY↗
DOE OSTI2021

Reactive phosphine combinatorial co-sputtering of cation disordered ZnGeP2 films

The discovery of new materials by coupling high-throughput synthesis with computational screening is being increasingly adopted. However, thus far, phosphides have been largely overlooked for both computational screening and high-throughput synthesis. In this paper, we report on the use of a high-throughput synthesis technique, reactive combinatorial co-sputtering with PH 3 , to deposit ZnGeP 2 thin films. We grew amorphous films over a wide range of compositions and found an upper limit in growth temperature determined by Zn and P volatility. We found that depositing in a Ge-limited regime could be utilized to slow the growth rate to compensate for the desorption of the Zn and P. Crystalline films were achieved by depositing films at higher temperatures in this Ge-limited regime with a reduced deposition rate. X-ray diffraction revealed that the films had crystallized in the zinc blende, cation-disordered structure. The crystalline films exhibited optical absorption energy threshold values ranging from 0.8 to 1.3 eV. Increased Ge content was found in films that exhibited a decreased absorption onset energy. Native defect calculations were used to gain an understanding of the off-stoichiometry seen in these films. This work provides the first high-throughput investigation of ZnGeP 2 , demonstrating the ability to grow amorphous and cation disordered ZnGeP 2 over a wide range of compositions with varying optical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Computational design of thermoelectric alloys through optimization of transport and dopability

Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Defect chemistry and doping of BiCuSeO

While p-type BiCuSeO is a well-known mid-temperature oxide thermoelectric (TE) material, computations predict that superior TE performance can be realized through n-type doping. In this study, we use first-principles defect calculations to show that Cu vacancies are responsible for the native p-type self doping; yet, we find that BiCuSeO is n-type dopable under Cu-rich growth conditions, where the formation of Cu vacancies is suppressed. We computationally survey a broad suite of 23 dopants and find that only Cl and Br are effective n-type dopants. Therefore, we recommend that future experimental doping efforts utilize phase boundary mapping to optimize the electron concentration and resolve the anomalous p–n–p transitions observed in halogen-doped BiCuSeO. The prospect of n-type doping, as revealed by our defect calculations, paves the path for rational design of BiCuSeO chemical analogues with similar doping behavior and even better TE performance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computationally Guided Discovery of Axis-Dependent Conduction Polarity in NaSnAs Crystals

Most electronic materials exhibit a single dominant charge carrier type, either holes or electrons, along all crystallographic directions. However, there are a small number of compounds, mostly metals, that exhibit simultaneous p-type and n-type conduction behavior along different crystallographic directions. We demonstrate that the experimental discovery of semiconductors with this axis-dependent conduction polarity can be facilitated by identifying a large anisotropy of either the electron or hole effective masses (m*) or both, providing the electron and hole masses dominate along different crystallographic directions. We calculated the layered semiconductor NaSnAs to have a lower electron m* in-plane than the cross-plane and a very large hole m* in-plane and small hole m* cross-plane. We established the growth of >3 mm-sized NaSnAs crystals via Sn flux and confirmed the band gap to be 0.65 eV, in agreement with theory. NaSnAs exhibits p-type thermopowers cross-plane and n-type thermopowers in-plane, confirming that the large anisotropy in the effective mass at the band edges is an excellent indicator for axis-dependent conduction polarity. Altogether, this work shows that the discovery of semiconductors with such a phenomenon can be accelerated by computationally evaluating the anisotropic curvatures of the band edges, paving the way for their future discovery and application.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Computational discovery of promising new n-type dopable ABX Zintl thermoelectric materials

Computational prediction of good thermoelectric (TE) performance in several n-type doped Zintl phases, combined with successful experimental realization, has sparked interest in discovering new n-type dopable members of this family of materials. However, most known Zintls are typically only p-type dopable; prior successes in finding n-type Zintl phases have been largely serendipitous. Here in this paper, we go beyond previously synthesized Zintl phases and perform chemical substitutions in known n-type dopable ABX Zintl phases to discover new ones. We use first-principles calculations to predict their stability, potential for TE performance as well as their n-type dopability. Using this approach, we find 17 new ABX Zintl phases in the KSnSb structure type that are predicted to be stable. Several of these newly predicted phases (KSnBi, RbSnBi, NaGeP) are found to exhibit promising n-type TE performance and are n-type dopable. We propose these compounds for further experimental studies, especially KSnBi and RbSnBi, which are both predicted to be good TE materials with high electron concentrations due to self-doping by native defects, when grown under alkali-rich conditions.

36 MATERIALS SCIENCE↗