Diamond like semiconductors (DLS) have emerged as candidates for thermoelectric energy conversion. Towards understanding and optimizing performance, we present a comprehensive investigation of the electronic properties of two DLS phases, quaternary Cu 2 HgGeTe 4 and related ordered vacancy compound Hg 2 GeTe 4 , including thermodynamic stability, defect chemistry, and transport properties. To establish the thermodynamic link between the related but distinct phases, the stability region for both is visualized in chemical potential space. In spite of their similar structure and bonding, we show that the two materials exhibit reciprocal behaviors for dopability. Cu 2 HgGeTe 4 is degenerately p-type in all environments despite its wide stability region, due to the presence of low-energy acceptor defects V Cu and Cu Hg and is resistant to extrinsic n-type doping. Meanwhile Hg 2 GeTe 4 has a narrow stability region and intrinsic behavior due to the relatively high formation energy of native defects, but presents an opportunity for bi-polar doping. While these two compounds have similar structure, bonding, and chemical constituents, the reciprocal nature of their dopability emerges from significant differences in band edge positions. A Brouwer band diagram approach is utilized to visualize the role of native defects on carrier concentrations, dopability, and transport properties. This study elucidates the doping asymmetry between two solid-solution forming DLS phases Cu 2 HgGeTe 4 and Hg 2 GeTe 4 by revealing the defect chemistry of each compound, and suggests design strategies for defect engineering of DLS phases.
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Empirical modeling of dopability in diamond-like semiconductors
Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both
- NSF-PAR ID:
- 10153904
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Ultrawide‐bandgap semiconductors such as AlN, BN, and diamond hold tremendous promise for high‐efficiency deep‐ultraviolet optoelectronics and high‐power/frequency electronics, but their practical application has been limited by poor current conduction. Through a combined theoretical and experimental study, it is shown that a critical challenge can be addressed for AlN nanostructures by using N‐rich epitaxy. Under N‐rich conditions, the p‐type Al‐substitutional Mg‐dopant formation energy is significantly reduced by 2 eV, whereas the formation energy for N‐vacancy related compensating defects is increased by ≈3 eV, both of which are essential to achieve high hole concentrations of AlN. Detailed analysis of the current−voltage characteristics of AlN p‐i‐n diodes suggests that current conduction is dominated by hole‐carrier tunneling at room temperature, which is directly related to the activation energy of Mg dopants. At high Mg concentrations, the dispersion of Mg acceptor energy levels leads to drastically reduced activation energy for a portion of Mg dopants, evidenced by the small tunneling energy of 67 meV, which explains the efficient current conduction and the very small turn‐on voltage (≈5 V) for the diodes made of nanoscale AlN. This work shows that nanostructures can overcome the dopability challenges of ultrawide‐bandgap semiconductors and significantly increase the efficiency of devices.
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It has been challenging to synthesize p-type SnOx(1≤x<2) and engineer the electrical properties such as carrier density and mobility due to the narrow processing window and the localized oxygen 2p orbitals near the valence band.
We recently reported on the processing of p-type SnOx and an oxide-based p-n heterostructures, demonstrating high on/off rectification ratio (>103), small turn-on voltage (<0.5 V), and low saturation current (~1×10-10A)1. In order to further understand the p-type oxide and engineer the properties for various electronic device applications, it is important to identify (or establish) the dominating doping and transport mechanisms. The low dopability in p-type SnOx, of which the causation is also closely related to the narrow processing window, needs to be mitigated so that the electrical properties of the material are to be adequately engineered2, 3.
Herein, we report on the multifunctional encapsulation of p-SnOxto limit the surface adsorption of oxygen and selectively permeate hydrogen into the p-SnOxchannel for thin film transistor (TFT) applications. Time-of-flight secondary ion mass spectrometry measurements identified that ultra-thin SiO2as a multifunctional encapsulation layer effectively suppressed the oxygen adsorption on the back channel surface of p-SnOxand augmented hydrogen density across the entire thickness of the channel. Encapsulated p-SnOx-based TFTs demonstrated much-enhanced channel conductance modulation in response to the gate bias applied, featuring higher on-state current and lower off-state current. The relevance between the TFT performance and the effects of oxygen suppression and hydrogen permeation is discussed in regard to the intrinsic and extrinsic doping mechanisms. These results are supported by density-functional-theory calculations.
Acknowledgement This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 20011028) by KRISS. K.N. was supported by Basic Science Research Program (NRF-2021R11A1A01051246) through the NRF Korea funded by the Ministry of Education.
References Lee, D. H.; Park, H.; Clevenger, M.; Kim, H.; Kim, C. S.; Liu, M.; Kim, G.; Song, H. W.; No, K.; Kim, S. Y.; Ko, D.-K.; Lucietto, A.; Park, H.; Lee, S., High-Performance Oxide-Based p–n Heterojunctions Integrating p-SnOx and n-InGaZnO.
ACS Applied Materials & Interfaces 2021, 13 (46), 55676-55686.Hautier, G.; Miglio, A.; Ceder, G.; Rignanese, G.-M.; Gonze, X., Identification and design principles of low hole effective mass p-type transparent conducting oxides.
Nat Commun 2013, 4 .Yim, K.; Youn, Y.; Lee, M.; Yoo, D.; Lee, J.; Cho, S. H.; Han, S., Computational discovery of p-type transparent oxide semiconductors using hydrogen descriptor.
npj Computational Materials 2018, 4 (1), 17.Figure 1
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Abstract Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.
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Abstract Generative models are a sub-class of machine learning models that are capable of generating new samples with a target set of properties. In chemical and materials applications, these new samples might be drug targets, novel semiconductors, or catalysts constrained to exhibit an application-specific set of properties. Given their potential to yield high-value targets from otherwise intractable design spaces, generative models are currently under intense study with respect to how predictions can be improved through changes in model architecture and data representation. Here we explore the potential of multi-task transfer learning as a complementary approach to improving the validity and property specificity of molecules generated by such models. We have compared baseline generative models trained on a single property prediction task against models trained on additional ancillary prediction tasks and observe a generic positive impact on the validity and specificity of the multi-task models. In particular, we observe that the validity of generated structures is strongly affected by whether or not the models have chemical property data, as opposed to only syntactic structural data, supplied during learning. We demonstrate this effect in both interpolative and extrapolative scenarios (i.e., where the generative targets are poorly represented in training data) for models trained to generate high energy structures and models trained to generated structures with targeted bandgaps within certain ranges. In both instances, the inclusion of additional chemical property data improves the ability of models to generate valid, unique structures with increased property specificity. This approach requires only minor alterations to existing generative models, in many cases leveraging prediction frameworks already native to these models. Additionally, the transfer learning strategy is complementary to ongoing efforts to improve model architectures and data representation and can foreseeably be stacked on top of these developments.
