Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data.

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

Low-Pressure Electrochemical Synthesis of Complex High-Pressure Superconducting Superhydrides.

There is great current interest in multicomponent superhydrides due to their unique quantum properties under pressure. A remarkable example is the ternary superhydride Li_{2}MgH_{16} computationally identified to have an unprecedented high superconducting critical temperature T_{c} of ∼470 K at 250 GPa. However, the very high synthesis pressures required remains a significant hurdle for detailed study and potential applications. In this Letter, we evaluate the feasibility of synthesizing ternary Li-Mg superhydrides by the recently proposed pressure-potential (P^{2}) method that uniquely combines electrochemistry and applied pressure to control synthesis and stability. The results indicate that it is possible to synthesize Li-Mg superhydrides at modest pressures by applying suitable electrode potentials. Using pressure alone, no Li-Mg ternary hydrides are predicted to be thermodynamically stable, but in the presence of electrode potentials, both Li_{2}MgH_{16} and Li_{4}MgH_{24} can be stabilized at modest pressures. Three polymorphs are predicted as ground states of Li_{2}MgH_{16} below 300 GPa, with transitions at 33 and 160 GPa. The highest pressure phase is superconducting, while the two at lower pressures are not. Our findings point out the potentially important role of the P^{2} method in controlling phase stability of complex multicomponent superhydrides.

Combining pressure and electrochemistry to synthesize superhydrides.

Recently, superhydrides have been computationally identified and subsequently synthesized with a variety of metals at very high pressures. In this work, we evaluate the possibility of synthesizing superhydrides by uniquely combining electrochemistry and applied pressure. We perform computational searches using density functional theory and particle swarm optimization calculations over a broad range of pressures and electrode potentials. Using a thermodynamic analysis, we construct pressure-potential phase diagrams and provide an alternate synthesis concept, pressure-potential ([Formula: see text]), to access phases having high hydrogen content. Palladium-hydrogen is a widely studied material system with the highest hydride phase being Pd3H4 Most strikingly for this system, at potentials above hydrogen evolution and ∼ 300 MPa pressure, we find the possibility to make palladium superhydrides (e.g., PdH10). We predict the generalizability of this approach for La-H, Y-H, and Mg-H with 10- to 100-fold reduction in required pressure for stabilizing phases. In addition, the [Formula: see text] strategy allows stabilizing additional phases that cannot be done purely by either pressure or potential and is a general approach that is likely to work for synthesizing other hydrides at modest pressures.

Uncertainty quantification of DFT-predicted finite temperature thermodynamic properties within the Debye model.

Density functional theory (DFT) calculations are routinely used to screen for functional materials for a variety of applications. This screening is often carried out with a few descriptors, which use ground-state properties that typically ignore finite temperature effects. Finite-temperature effects can be included by calculating the vibration properties, and this can greatly improve the fidelity of computational screening. An important challenge for DFT-based screening is the sensitivity of the predictions to the choice of the exchange correlation function. In this work, we rigorously explore the sensitivity of finite temperature thermodynamic properties to the choice of the exchange correlation functional using the built-in error estimation capabilities within the Bayesian Error Estimation Functional-van der Waals (BEEF-vdW). The vibrational properties are estimated using the Debye model, and we quantify the uncertainty associated with finite-temperature properties for a diverse collection of materials. We find good agreement with experiment and small spread in predictions over different exchange correlation functionals for Mg, Al2O3, Al, Ca, and GaAs. In the case of Li, Li2O, and NiO, however, we find a large spread in predictions as well as disagreement between experiment and functionals due to complex bonding environments. While the energetics generated by the BEEF-vdW ensemble is typically normal, the complex mapping through the Debye model leads to the derived finite temperature properties having non-Gaussian behavior. We test a wide variety of probability distributions that best represent the finite temperature distribution and find that properties such as specific heat, Gibbs free energy, entropy, and thermal expansion coefficient are well described by normal or transformed normal distributions, while the prediction spread of volume at a given temperature does not appear to be drawn from a single distribution. Given the computational efficiency of the approach, we believe that uncertainty quantification should be routinely incorporated into finite-temperature predictions. In order to facilitate this, we have open-sourced the code base under the name dePye.

A hybrid functional study of native point defects in Cu2SnS3: implications for reducing carrier recombination.

The native point defects in the earth-abundant solar material Cu2SnS3 are studied using the hybrid functional. To generate more accurate formation energies of defects, the extended Freysoldt, Neugebauer, and Van de Walle (FNV) method is used for finite-size corrections in the charged supercell calculations. According to the calculated defect energetics, it is found that the usual experimental conditions can lead to abundant deep centers that deteriorate solar cell performance. To reduce the carrier recombination caused by the deep centers, Sn-rich and S-poor conditions should be attempted. The present calculations also give satisfactory explanations for a recent experimental work on the defect levels in Cu2SnS3.