Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN

Manna, Sukriti; Brennecka, Geoff L.; Stevanović, Vladan; Ciobanu, Cristian V.

doi:10.1063/1.4993254

Cited by 65 publications

(55 citation statements)

References 40 publications

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“…The decrease in frequency can be mitigated by co-doping with a small trivalent element (boron), as shown for other doped AlN alloys. 15 Last but not least, it is worth noting that doping with Cr could enable magnetic polarizaton of the Cr ions in the wurtzite lattice and/or of the minority carriers: these effects are non-existent in pure AlN, and could be pursued for spintronic applications or for low-hysteresis magnets. 25 The significant increase of piezoelectric modulus reported here provides significant drive to pursue the two directions identified above, and overcome routine barriers towards establishing Cr-AlN as a replacement for AlN with large performance enhancements.…”

Section: Discussionmentioning

confidence: 99%

“…The wurzite phase is found to be favorable up to x = 0.25, beyond which rocksalt alloys are stable; this wurzite to rocksalt phase transition point is consistent with previous experimental observations and other theoretical predictions. 19,20 We have compared the mixing enthalpy of the Cr x Al 1−x N alloys with that of several other common wurzite-based nitrides, 13,15,18,36,37 Sc x Al 1−x N, Y x Al 1−x N, and Y x In 1−x N, with the results shown in Figure 2(b). The mixing enthalpies are positive for all cases, meaning that the alloying of AlN or InN with their respective end members is an endothermic process.…”

Section: A Enthalpy Of Mixingmentioning

confidence: 99%

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Enhanced Piezoelectric Response of AlN via CrN Alloying

et al. 2018

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Since AlN has emerged as an important piezoelectric material for a wide variety of applications, efforts have been made to increase its piezoelectric response via alloying with transition metals that can substitute for Al in the wurtzite lattice. Herein, we report density functional theory calculations of structure and properties of the Cr-AlN system for Cr concentrations ranging from zero to beyond the wurtzite-rocksalt transition point. By studying the different contributions to the longitudinal piezoelectric coefficient, we propose that the physical origin of the enhanced piezoelectricity in CrxAl1−xN alloys is the increase of the internal parameter u of the wurtzite structure upon substitution of Al with the larger Cr ions. Among a set of wurtzite-structured materials, we have found that Cr-AlN has the most sensitive piezoelectric coefficient with respect to alloying concentration. Based on these results, we propose that Cr-AlN is a viable piezoelectric material whose properties can be tuned via Cr composition. We support this proposal by combinatorial synthesis experiments, which show that Cr can be incorporated in the AlN lattice up to 30% before a detectable transition to rocksalt occurs. At this Cr content, the piezoelectric modulus d33 is approximately four times larger than that of pure AlN. This finding, combined with the relative ease of synthesis under nonequilibrium conditions, may propel Cr-AlN as a prime piezoelectric material for applications such as resonators and acoustic wave generators.

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Section: Discussionmentioning

confidence: 99%

Section: A Enthalpy Of Mixingmentioning

confidence: 99%

Enhanced Piezoelectric Response of AlN via CrN Alloying

et al. 2018

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“…Since the initial report 29 Alloying in small amounts of B, which shares the +3 oxidation state, but forms much stronger chemical bonds with N, is predicted to counteract the decrease in stiffness caused in the crystal by Sc or Y substitution alone. This is expected to maintain a high electromechanical coupling coefficient, which is attractive for BAW filters 32 . Instead of relying on the +3 oxidation state Sc or Y isovalent with Al, it has been predicted that codoping AlN with Mg of oxidation state +2 and Hf or Zr of oxidation state +4 in 1:1 ratio can lead to a giant enhancement in the piezoelectricity of AlN 33 .…”

Section: Nitride Extreme-piezoelectrics and Ferroelectricsmentioning

confidence: 99%

The new nitrides: layered, ferroelectric, magnetic, metallic and superconducting nitrides to boost the GaN photonics and electronics eco-system

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et al. 2019

Jpn. J. Appl. Phys.

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The nitride semiconductor materials GaN, AlN, and InN, and their alloys and heterostructures have been investigated extensively in the last 3 decades, leading to several technologically successful photonic and electronic devices. Just over the past few years, a number of "new" nitride materials have emerged with exciting photonic, electronic, and magnetic properties. Some examples are 2D and layered hBN and the III-V diamond analog cBN, the transition metal nitrides ScN, YN, and their alloys (e.g. ferroelectric ScAlN), piezomagnetic GaMnN, ferrimagnetic Mn4N, and epitaxial superconductor/semiconductorNbN/GaN heterojunctions. This article reviews the fascinating and emerging physics and science of these new nitride materials. It also discusses their potential applications in future generations of devices that take advantage of the photonic and electronic devices eco-system based on transistors, light-emitting diodes, and lasers that have already been created by the nitride semiconductors.

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“…A relatively high tolerance of 10 À 6 eV for energy convergence was employed in these calculations. Three independent elastic constants as shown in Table 1, n atoms � n Kpoints � 1000, were determined for cubic system by employing suitable lattice distortions [45,37,65,38,39,40] represented by a strain tensor, e, (defined by equation 1) in such a way that the new lattice vectors r in the distorted lattice is given by r 0 ¼ ðI þ eÞr where I is the unit matrix.…”

Section: Dft Calculationsmentioning

confidence: 99%

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

et al. 2020

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Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (� 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on-thefly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (�1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with �500 total reference calculations. Using an extensive DFT test set of~1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/ Å of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

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Tuning the piezoelectric and mechanical properties of the AlN system via alloying with YN and BN

Cited by 65 publications

References 40 publications

Enhanced Piezoelectric Response of AlN via CrN Alloying

Enhanced Piezoelectric Response of AlN via CrN Alloying

The new nitrides: layered, ferroelectric, magnetic, metallic and superconducting nitrides to boost the GaN photonics and electronics eco-system

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

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