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 Al1-xScxN 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 Al1-xScxN 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.

A database of computed Raman spectra of inorganic compounds with accurate hybrid functionals.

Raman spectroscopy is widely applied in identifying local structures in materials, but the interpretation of Raman spectra is non-trivial. An accurate computational database of reference spectra calculated with a consistent level of theory can significantly aid in interpreting measured Raman spectra. Here, we present a database of Raman spectra of inorganic compounds calculated with accurate hybrid functionals in density functional theory. Raman spectra were obtained by calculating dynamical matrices and polarizability tensors for structures from the Inorganic Crystal Structure Database. The calculated Raman spectra and other phonon properties (e.g., infrared spectra) are stored in a MongoDB database publicly shared through a web application. We assess the accuracy of our Raman calculations by statistically comparing ~80 calculated spectra with an existing experimental Raman database. To date, the database contains 161 compounds and is continuously growing as we add more materials computed with our automated workflow.

Material descriptors for thermoelectric performance of narrow-gap semiconductors and semimetals.

Thermoelectric (TE) cooling is an environment-friendly alternative to vapor compression cooling. New TE materials with high coefficients of performance are needed to further advance this technology. Narrow-gap semiconductors and semimetals have garnered interest for Peltier cooling, yet large-scale computational searches often rely on material descriptors that do not account for bipolar conduction effects. In this work, we derive three material descriptors to assess the TE performances of narrow-gap semiconductors and semimetals - band gap, n- and p-type TE quality factors, and the asymmetry in transport between the majority and minority carriers. We show that a large asymmetry is critical to achieving high TE performance through minimization of bipolar conduction effects. We validate the predictive power of the descriptors by correctly identifying Mg3Bi2 and Bi2Te3 as high-performing room-temperature TE materials. By applying these descriptors to a broad set of 650 Zintl phases, we identify three candidate room-temperature TE materials, namely SrSb2, Zn3As2, and NaCdSb. The proposed material descriptors will enable fast, targeted searches of narrow-gap semiconductors and semimetals for low-temperature TEs. We further propose a refined TE quality factor, Bbp, which is a composite descriptor of the peak zT in materials exhibiting significant bipolar conduction; Bbp can be used to compare the TE performances of narrow-gap semiconductors.

A search for new back contacts for CdTe solar cells.

There is widespread interest in reaching the practical efficiency of cadmium telluride (CdTe) thin-film solar cells, which suffer from open-circuit voltage loss due to high surface recombination velocity and Schottky barrier at the back contact. Here, we focus on back contacts in the superstrate configuration with the goal of finding new materials that can provide improved passivation, electron reflection, and hole transport properties compared to the commonly used material, ZnTe. We performed a computational search among 229 binary and ternary tetrahedrally bonded structures using first-principles methods and transport models to evaluate critical material design criteria, including phase stability, electronic structure, hole transport, band alignments, and p-type dopability. Through this search, we have identified several candidate materials and their alloys (AlAs, AgAlTe2, ZnGeP2, ZnSiAs2, and CuAlTe2) that exhibit promising properties for back contacts. We hope that these new material recommendations and associated guidelines will inspire new directions in hole transport layer design for CdTe solar cells.

Upper-Bound Energy Minimization to Search for Stable Functional Materials with Graph Neural Networks.

The discovery of new materials in unexplored chemical spaces necessitates quick and accurate prediction of thermodynamic stability, often assessed using density functional theory (DFT), and efficient search strategies. Here, we develop a new approach to finding stable inorganic functional materials. We start by defining an upper bound to the fully relaxed energy obtained via DFT as the energy resulting from a constrained optimization over only cell volume. Because the fractional atomic coordinates for these calculations are known a priori, this upper bound energy can be quickly and accurately predicted with a scale-invariant graph neural network (GNN). We generate new structures via ionic substitution of known prototypes, and train our GNN on a new database of 128 000 DFT calculations comprising both fully relaxed and volume-only relaxed structures. By minimizing the predicted upper-bound energy, we discover new stable structures with over 99% accuracy (versus DFT). We demonstrate the method by finding promising new candidates for solid-state battery (SSB) electrolytes that not only possess the required stability, but also additional functional properties such as large electrochemical stability windows and high conduction ion fraction. We expect this proposed framework to be directly applicable to a wide range of design challenges in materials science.

Y2Te3: A New n-Type Thermoelectric Material.

Rare-earth chalcogenides Re3-xCh4 (Re = La, Pr, Nd, Ch = S, Se, and Te) have been extensively studied as high-temperature thermoelectric (TE) materials owing to their low lattice thermal conductivity (κL) and tunable electron carrier concentration via cation vacancies. In this work, we introduce Y2Te3, a rare-earth chalcogenide with a rocksalt-like vacancy-ordered structure, as a promising n-type TE material. We computationally evaluate the transport properties, optimized TE performance, and doping characteristics of Y2Te3. Combined with a low κL, multiple low-lying conduction band valleys yield a high n-type TE quality factor. We find that a maximum figure of merit zT > 1 can be achieved when Y2Te3 is optimally doped to an electron concentration of 1-2 × 1020 cm-3. We use defect calculations to show that Y2Te3 is n-type dopable under Y-rich growth conditions, which suppress the formation of acceptor-like cation vacancies. Furthermore, we propose that optimal n-type doping can be achieved with halogens (Cl, Br, and I), with I being the most effective dopant. Our computational results as well as experimental results reported elsewhere motivate further optimization of Y2Te3 as an n-type TE material.

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 Ba2(1-x)Sr2xCdP2 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. 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.

Predicting energy and stability of known and hypothetical crystals using graph neural network.

The discovery of new inorganic materials in unexplored chemical spaces necessitates calculating total energy quickly and with sufficient accuracy. Machine learning models that provide such a capability for both ground-state (GS) and higher-energy structures would be instrumental in accelerated screening. Here, we demonstrate the importance of a balanced training dataset of GS and higher-energy structures to accurately predict total energies using a generic graph neural network architecture. Using ∼ 16,500 density functional theory calculations from the National Renewable Energy Laboratory (NREL) Materials Database and ∼ 11,000 calculations for hypothetical structures as our training database, we demonstrate that our model satisfactorily ranks the structures in the correct order of total energies for a given composition. Furthermore, we present a thorough error analysis to explain failure modes of the model, including both prediction outliers and occasional inconsistencies in the training data. By examining intermediate layers of the model, we analyze how the model represents learned structures and properties.

Experimental and computational phase boundary mapping of Co4Sn6Te6.

Binary Co4Sb12 skutterudite (also known as CoSb3) has been extensively studied; however, its mixed-anion counterparts remain largely unexplored in terms of their phase stability and thermoelectric properties. In the search for complex anionic analogs of the binary skutterudite, we begin by investigating the Co4Sb12-Co4Sn6Te6 pseudo-binary phase diagram. We observe no quaternary skutterudite phases and as such, focus our investigations on the ternary Co4Sn6Te6 via experimental phase boundary mapping, transport measurements, and first-principles calculations. Phase boundary mapping using traditional bulk syntheses reveals that the Co4Sn6Te6 exhibits electronic properties ranging from a degenerate p-type behavior to an intrinsic behavior. Under Sn-rich conditions, Hall measurements indicate degenerate p-type carrier concentrations and high hole mobility. The acceptor defect SnTe, and donor defects TeSn and Coi are the predominant defects and rationally correspond to regions of high Sn, Te, and Co, respectively. Consideration of the defect energetics indicates that p-type extrinsic doping is plausible; however, SnTe is likely a killer defect that limits n-type dopability. We find that the hole carrier concentration in Co4Sn6Te6 can be further optimized by extrinsic p-type doping under Sn-rich growth conditions.

Thermoelectricity in transition metal compounds: the role of spin disorder.

At room temperature and above, most magnetic materials adopt a spin-disordered (paramagnetic) state whose electronic properties can differ significantly from their low-temperature, spin-ordered counterparts. Yet computational searches for new functional materials usually assume some type of magnetic order. In the present work, we demonstrate a methodology to incorporate spin disorder in computational searches and predict the electronic properties of the paramagnetic phase. We implement this method in a high-throughput framework to assess the potential for thermoelectric performance of 1350 transition-metal sulfides and find that all magnetic systems we identify as promising in the spin-ordered ground state cease to be promising in the paramagnetic phase due to disorder-induced deterioration of the charge carrier transport properties. We also identify promising non-magnetic candidates that do not suffer from these spin disorder effects. In addition to identifying promising materials, our results offer insights into the apparent scarcity of magnetic systems among known thermoelectrics and highlight the importance of including spin disorder in computational searches.

Mechanism and energetics of O and O2 adsorption on polar and non-polar ZnO surfaces.

Polar surfaces of semiconducting metal oxides can exhibit structures and chemical reactivities that are distinct from their non-polar surfaces. Using first-principles calculations, we examine O adatom and O2 molecule adsorption on 8 different known ZnO reconstructions including Zn-terminated (Zn-ZnO) and O-terminated (O-ZnO) polar surfaces, and non-polar surfaces. We find that adsorption tendencies are largely governed by the thermodynamic environment, but exhibit variations due to the different surface chemistries of various reconstructions. The Zn-ZnO surface reconstructions which appear under O-rich and H-poor environments are found to be most amenable to O and O2 adsorption. We attribute this to the fact that on Zn-ZnO, the O-rich environments that promote O adsorption also simultaneously favor reconstructions that involve adsorbed O species. On these Zn-ZnO surfaces, O2 dissociatively adsorbs to form O adatoms. By contrast, on O-ZnO surfaces, the O-rich conditions required for O or O2 adsorption tend to promote reconstructions involving adsorbed H species, making further O species adsorption more difficult. These insights about O2 adsorption on ZnO surfaces suggest possible design rules to understand the adsorption properties of semiconductor polar surfaces.

A novel, layered phase in Ti-rich SrTiO3 epitaxial thin films.

Sr2Ti7O14, a new phase, is synthesized by leveraging the innate chemical and thermo-dynamic instabilities in the SrTiO3-TiO2 system and non-equilibrium growth techniques. The chemical composition, epitaxial relationships, and orientation play roles in the formation of this novel layered phase, which, in turn, possesses unusual charge ordering, anti-ferromagnetic ordering, and low, glass-like thermal conductivity.