Engineering topics
Publications and source records attributed to Wolverton, Chris.
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a federation of databases, enhancing the accessibility and discoverability of materials and chemical data.
Hydrogen economy is of paramount importance in the global transition to a sustainable, clean energy source that contributes to decarbonization efforts. In particular, the proton conducting proton ceramic fuel cells (PCFCs) play a crucial role in promoting hydrogen energy technology [1-5]. In a PCFC, the electrolyte is typically a solid oxide material that enables proton transport and can operate at temperatures lower than those of traditional oxygen ion conducting fuel cells [6]. The reduced operating temperature of PCFCs contributes to their durability, scalability, and efficiency [7,8]. BaZr 0.8 Y 0.2 O 3-x (BZY) is a promising proton conducting solid oxide electrolyte [9,10]. The emphasis on proton conductivity entails the importance of understanding proton content in the system. However, previous studies have largely relied on bulk techniques such as electrochemical impedance spectroscopy [7,11], Karl-Fischer titration [12,13], and thermogravimetric analysis [14] to obtain proton concentration. This is due to the small atomic size and light mass of hydrogen species making direct detection challenging. Bulk methods may be useful in estimating the proton content; however, it overlooks possible proton concentration gradient or segregation that may occur across or within a nanoparticle, especially when proton incorporation occurs nonuniformly through steam exposure on powder samples. In this paper, BZY is protonated at an estimated 0.15 mol of protons via steam exposure. Electron energy loss spectroscopy (EELS) with nanoscale spatial resolution is explored to identify proxy signals for proton detection using a JEOL ARM300 microscope operated at 300 kV with a Gatan GIF Continuum detector.
Oxygen vacancies are a common defect in oxides and play vital roles in many technological applications such as oxygen separation, catalytic reactors, solid oxide fuel cells, and solar thermochemical water splitting. The oxygen vacancy formation energy is directly related to the oxide reduction enthalpy and the vacancy concentration and, therefore, is a key quantity underlying these applications. The complexity of measuring oxygen vacancy formation energies experimentally suggests the utility of calculations based on density functional theory (DFT). However, the calculated results are strongly dependent on the exchange-correlation functionals and other parameters such as the Hubbard U . In this work, we compare the performance of the meta-GGA strongly constrained and appropriately normed (SCAN) functional and the commonly used semilocal generalized gradient approximation (GGA) functionals with experimental values of structural parameters, band gaps, magnetic structures, and oxygen vacancy formation energies for six representative oxides, i.e., CaMnO 3 , SrMnO 3 , LaMnO 3 , YMnO 3 , LaFeO 3 , and CeO 2 . Further, our results show that SCAN usually has better agreement with the experimental lattice constants and band gaps and larger magnetic moments than the commonly used GGA functionals. Although SCAN overestimates the oxygen vacancy formation energies of transition metal oxides and therefore requires unusually large Hubbard U values to reproduce the experimental reduction enthalpies, it does predict the correct oxygen vacancy formation energy of CeO 2 , which challenges the commonly used GGA and hybrid functionals. Our results underscore the challenges that exist in describing these complex oxides by DFT and may shed light on developing more accurate exchange-correlation functionals for oxygen vacancy formation energy calculations.
Here, we propose a first-principles model of minimum lattice thermal conductivity ($κ^{min}_L$) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and find a universal behavior of $κ^{min}_L$ in crystals in the high-temperature limit: The isotropically averaged $κ^{min}_L$ is independent of structural complexity and bounded within a range from ~0.1 to ~2.6 W/(m K), in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound, $κ^{min}_L$ is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated $κ^{min}_L$ in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill–Watson–Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow κ L . Our work offers a unified understanding of $κ^{min}_L$, which can guide the rational engineering of materials to achieve .
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The isostructural heteroanionic compounds β-LiAsS 2–x Se x (x = 0, 0.25, 1, 1.75, 2) show a positive correlation between selenium content and second-harmonic response and greatly outperform the industry standard AgGaSe 2 . These materials crystallize in the noncentrosymmetric space group Cc as one-dimensional $\frac{1}{∞}$ [AsQ 2 ] - (Q = S, Se, S/Se) chains consisting of corner-sharing AsQ3 trigonal pyramids with charge-balancing Li + atoms interspersed between the chains. LiAsS 2–x Se x melts congruently for 0 ≤ x ≤ 1.75, but when the Se content exceeds x = 1.75, crystallization is complicated by a phase transition. This behavior is attributed to the β- to α-phase transition present in LiAsSe 2 , which is observed in the Se-rich compositions. The band gap decreases with increasing Se content, starting at 1.63 eV (LiAsS 2 ) and reaching 1.06 eV (β-LiAsSe 2 ). Second-harmonic generation measurements as a function of wavelength on powder samples of β-LiAsS 2–x Se x show that these materials exhibit significantly higher nonlinearity than AgGaSe 2 (d 36 = 33 pm/V), reaching a maximum of 61.2 pm/V for LiAsS 2 . In comparison, single-crystal measurements for LiAsSSe yielded a d eff = 410 pm/V. LiAsSSe, LiAsS 0.25 Se 1.75 , and β-LiAsSe 2 show phase-matching behavior for incident wavelengths exceeding 3 μm. The laser-induced damage thresholds from two-photon absorption processes are on the same order of magnitude as AgGaSe 2 , with S-rich materials slightly outperforming AgGaSe 2 and Se-rich materials slightly underperforming AgGaSe 2 .
The behavior of 5f electrons in soft ligand environments makes actinides, and especially transuranium chalcogenides, an intriguing class of materials for fundamental studies. Due to the affinity of actinides for oxygen, however, it is a challenge to synthesize actinide chalcogenides using non-metallic reagents. Using the Boron Chalcogen Mixture (BCM) method, we achieved the synthesis of the transuranium sulfide NaCuNpS 3 starting from the oxide reagent, NpO 2 . Via the same synthetic route, the isostructural composition of NaCuUS 3 was synthesized and the material contrasted with NaCuNpS 3 . Single crystals of the U-analog, NaCuUS 3 , were found to undergo an unexpected reversible hydration process to form NaCuUS 3 ·xH 2 O (x ≈ 1.5). Here, a large combination of techniques was used to fully characterize the structure, hydration process, and electronic structures, specifically a combination of single crystal, powder, high temperature powder X-ray diffraction, extended X-ray ab-sorption fine structure, infrared, and inductively coupled plasma spectroscopies, thermogravimetric analysis, and density functional theory calculations. The outcome of these analyses enabled us to determine the composition of NaCuUS 3 ·xH 2 O and obtain a structural model that demonstrated the retention of the local structure within the [CuUS 3 ] – layers throughout the hydration-dehydration process. Band structure, density of states, and Bader charge calculations for NaCuUS 3 , NaCu-US 3 ·xH 2 O, and NaCuNpS 3 along with X-ray absorption near edge structure, UV-vis-NIR and work function measurements on ACuUS 3 (A = Na, K, and Rb) and NaCuUS 3 ·xH 2 O samples were carried out to demonstrate that electronic properties arise from the [CuTS 3 ] – layers and show surprisingly little dependence on the interlayer distance.
Abstract Density functional theory (DFT) has been widely applied in modern materials discovery and many materials databases, including the open quantum materials database (OQMD), contain large collections of calculated DFT properties of experimentally known crystal structures and hypothetical predicted compounds. Since the beginning of the OQMD in late 2010, over one million compounds have now been calculated and stored in the database, which is constantly used by worldwide researchers in advancing materials studies. The growth of the OQMD depends on project-based high-throughput DFT calculations, including structure-based projects, property-based projects, and most recently, machine-learning-based projects. Another major goal of the OQMD is to ensure the openness of its materials data to the public and the OQMD developers are constantly working with other materials databases to reach a universal querying protocol in support of the FAIR data principles.
Accurately predicting the formation energy of a compound, which describes its thermodynamic stability, is a key challenge in materials physics. Here, we employ a many-body quantum Monte Carlo (QMC) with single-reference trial functions to compute the formation energy of two electronically disparate compounds, the intermetallic VPt2 and the semiconductor CuI, for which standard density functional theory (DFT) predictions using both the Perdew-Burke-Ernzerhof (PBE) and the strongly constrained and appropriately normed (SCAN) density functional approximations deviate markedly from available experimental values. For VPt2, we find an agreement between QMC, SCAN, and PBE0 estimates, which therefore remain in disagreement with the much less exothermic experimental value. For CuI, the QMC result agrees with neither SCAN nor PBE pointing towards DFT exchange-correlation biases, likely related to the localized Cu 3d electrons. Compared to the behavior of some density functional approximations within DFT, spin-averaged QMC exhibits a smaller but still appreciable deviation when compared to experiment. The QMC result is slightly improved by incorporating spin-orbit corrections for CuI and solid I 2 , so that experiment and theory are brought into imperfect but reasonable agreement within about 120 meV/atom.
Super alkali-rich materials (alkali:transition metal ratio of ≥2:1), such as Li 5 FeO 4 , exhibit the potential to realize anionic redox upon deep delithiation. Li 5 FeO 4 was shown to undergo reversible cycling between Li 4 FeO 3.5 and Li 3 FeO 3.5 with combined cation/anion redox, a remarkable capacity of 189 mAh/g, and no O 2 release. However, the impact of phase transformations on the reaction thermodynamics and the correlation between the structural changes and reaction reversibility remain unclear. In this study, we use first-principles calculations to examine the delithiation and (re)lithiation reactions of Li 5 FeO 4 . We show that the experimentally observed charge and discharge processes go through non-equilibrium pathways. Upon delithiation, the compound undergoes a phase transformation from Li 5 FeO 4 , with tetrahedrally coordinated (T d ) Fe ions, to a delithiated disordered rocksalt structure, with octahedral (O h ) Fe ions. Fe-ion migration has an asymmetric kinetic barrier that makes T d → O h migration facile, whereas the reverse process has a much larger barrier, explaining the difficulties in reaction reversibility. We further elucidate the transition metal and O redox sequences during the charge cycle and identify the complex electrochemistry associated with the dual participation of cationic redox (Fe 3+ /Fe 4+ ) and anionic redox (O 2– /O – /O o ). Armed with this knowledge, we conduct high-throughput screening of known alkali-rich transition metal oxides by evaluating their potential to enable reversible anionic redox, with multiple candidates proposed for further experimental trials. Furthermore, our work provides a useful guide for the further development of super alkali-rich anionic-redox-active electrodes for high-energy-density batteries.
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.
We design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q 3 (A/M/M' = alkali, alkaline earth, post-transition metals, lanthanides, and Q = chalcogens). These compounds are shown to possess ultralow lattice thermal conductivity (κ l ), a desired requirement for thermal-barrier coatings and thermoelectrics. Upon screening the thermodynamic stability of ~1 million compounds using the ML model iteratively and performing density-functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their κ l using Peierls–Boltzmann transport equation, which reveals ultralow κ l (<2 Wm -1 K -1 at room temperature) due to their soft elasticity and strong phonon anharmonicity. Our work demonstrates the high efficiency of scale-invariant ML model in predicting novel compounds and presents experimental-research opportunities with these new compounds.
Twenty-seven new members of the A 2 Cu 2n Ln 4 Q 7+n (A = Cs, Rb; Ln = La-Nd, Sm, Gd-Yb; Q = S, Se) homologous series were synthesized in one of three structural types (indicated by n = 1, 2, 3). All the compounds contained 3D frameworks with alkali-metal-containing tunnels. For each increment in n, one Cu 2 Q was added, which was incorporated into the framework as an edge-sharing tetrahedron by replacing a square planar chalcogenide site. High-throughput DFT calculations predicted many of the phases to be thermodynamically stable. These predictions were compared with the synthesis results for the phases formed in each composition space. In the syntheses, heavier lanthanides showed a preference to start forming the n = 3 ACu 3 Ln 2 Q 5 , which is consistent with the predictions. RbCuNd 2 Se 4 and RbCuTb 2 Se 4 were found to be thermally stable under vacuum at temperatures up to 1000 °C. Optical measurements revealed band gaps of 1.55(5) and 1.62(5) eV for CsCuCe 2 Se 4 and RbCuTb 2 Se 4 , respectively, and a work function of 4.83(5) eV for CsCuPr 2 Se 4 . Additionally, some n = 3 ACu 3 Ln 2 Qs compounds exhibit a negative phonon mode because of a copper atom coordination, which may distort to a trigonal planar geometry at sufficiently low temperatures. The dynamic instabilities and the predicted distortion in the copper tetrahedra for the n = 3 ACu 3 Ln 2 Q 5 compounds were found to have a linear relationship with the atomic number of the lanthanides and the electronegativity of the lanthanides. In conclusion, the A 2 Cu 2 n Ln 4 Q 7+n compounds can potentially find application as high-temperature thermoelectric materials and other semiconductors.
Although BiAgOSe, an analogue of a well-studied thermoelectric material BiCuOSe, is thermodynamically stable, its synthesis is complicated by the low driving force of formation from the stable binary and ternary intermediates. Here we have developed a “subtraction strategy” to suppress byproducts and produce pure phase BiAgOSe using hydrothermal methods. Electronic structure calculations and optical characterization show that BiAgOSe is an indirect bandgap semiconductor with a bandgap of 0.95 eV. The prepared sample exhibits lower lattice thermal conductivities (0.61 W·m –1 ·K –1 at room temperature and 0.35 W·m –1 ·K –1 at 650 K) than BiCuOSe. Further, lattice dynamical simulations and variable temperature diffraction measurements demonstrate that the low lattice thermal conductivity arises from both the low sound velocity and high phonon–phonon scattering rates in BiAgOSe. These in turn result primarily from the soft Ag–Se bonds in the edge-sharing AgSe 4 tetrahedra and large sublattice mismatch between the quasi-two-dimensional [Bi 2 O 2 ] 2+ and [Ag 2 Se 2 ] 2– layers. These results highlight the advantages of manipulating the chemistry of homoleptic polyhedra in heteroanionic compounds for electronic structure and phonon transport control.
Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and thermal barrier coatings. Although the crystal structure and chemical bonding are known to play vital roles in shaping heat transfer behavior, material design approaches of lowering lattice thermal conductivity using chemical bonding principles are uncommon. In this work, an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles is presented and a high-efficiency approach of discovering low κ L materials by screening the local coordination environments of crystalline compounds is developed. The resulting first-principles calculations uncover 30 hitherto unexplored compounds with (ultra)low lattice thermal conductivities from 13 prototype crystal structures contained in the Inorganic Crystal Structure Database. Furthermore, an approach of rationally designing high-performance thermoelectrics is demonstrated by additionally incorporating cations with stereochemically active lone-pair electrons. Here these results not only provide atomic-level insights into the physical origin of the low lattice thermal conductivity in a large family of copper/silver-based compounds but also offer an efficient approach to discover and design materials with targeted thermal transport properties.
Solid-state lithium ion batteries have enhanced thermal stability compared to batteries using liquid electrolytes. Although their practical implementation is limited by undesired side reactions between the electrode and electrolyte, different modification strategies have been proposed to stabilize these reactive interfaces. These approaches have been primarily based upon bulk materials properties, however, which may not necessarily be representative of the chemistry at the solid/solid interface. In this work, first principles calculations and X-ray scattering experiments are used to elucidate molecular-level reactivity and develop design principles for tailored interfaces based on surface and interfacial properties. Because of its well-known instability, the interface between the Li metal anode and the lithium lanthanum titanate (LLTO) solid electrolyte is applied as a model system. Density functional theory calculations are used to describe bulk, surface, and interfacial thermochemistry of the Li/LLTO system, and interfacial reconstruction is probed using ab initio molecular dynamics. These simulations of the Li/LLTO interface demonstrate facile decomposition concomitant with reduction of Ti 4+ ions. Based on further insights from computational analysis of the surface band edge positions, La 2 O 3 is proposed as an interlayer coating and is shown to provide an energetic barrier for interfacial charge transfer and decomposition reactions from X-ray reflectivity measurements and theoretical calculations. The findings in this work suggest that design strategies based on surface and interfacial properties can be used to kinetically stabilize electrode/electrolyte interfaces in solid-state batteries.