Implicit solvation model for density-functional study of nanocrystal surfaces and reaction pathways

Solid-liquid interfaces are at the heart of many modern-day technologies and provide a challenge to many materials simulation methods. A realistic first-principles computational study of such systems entails the inclusion of solvent effects. In this work we implement an implicit solvation model that has a firm theoretical foundation into the widely used density-functional code VASP. The implicit solvation model follows the framework of joint density functional theory. We describe the framework, our algorithm and implementation, and benchmarks for small molecular systems. We apply the solvation model to study the surface energies of different facets of semiconducting and metallic nanocrystals and the S$_{text{N}} 2$ reaction pathway. We find that solvation reduces the surface energies of the nanocrystals, especially for the semiconducting ones and increases the energy barrier of the S$_{text{N}} 2$ reaction.

Computationally-Driven Experimental Discovery of the CeIr$_4$In Compound

We present a combined experimental and computational methodology for the discovery of new materials. Density functional theory (DFT) formation energy calculations allow us to predict the stability of various hypothetical structures. We demonstrate this approach by computationally predicting the Ce-Ir-In ternary phase diagram. We predict previously-unknown compounds CeIr$_4$In and Ce$_2$Ir$_2$In to be stable. Subsequently, we successfully synthesize CeIr$_4$In and characterize it by X-ray diffraction. Magnetization and heat capacity measurements of CeIr$_4$In are reported. The correct prediction and discovery of CeIr$_4$In validates this approach for discovering new materials.

Accuracy of Quantum Monte Carlo Methods for Point Defects in Solids

Quantum Monte Carlo approaches such as the diffusion Monte Carlo (DMC) method are among the most accurate many-body methods for extended systems. Their scaling makes them well suited for defect calculations in solids. We review the various approximations needed for DMC calculations of solids and the results of previous DMC calculations for point defects in solids. Finally, we present estimates of how approximations affect the accuracy of calculations for self-interstitial formation energies in silicon and predict DMC values of 4.4(1), 5.1(1) and 4.7(1) eV for the X, T and H interstitial defects, respectively, in a 16(+1)-atom supercell.

Phase transformation in Si from semiconducting diamond to metallic beta-Sn phase in QMC and DFT under hydrostatic and anisotropic stress

Silicon undergoes a phase transition from the semiconducting diamond phase to the metallic beta-Sn phase under pressure. We use quantum Monte Carlo calculations to predict the transformation pressure and compare the results to density functional calculations employing the LDA, PBE, PW91, WC, AM05, PBEsol and HSE06 exchange-correlation functionals. Diffusion Monte Carlo predicts a transition pressure of 14.0 +- 1.0 GPa slightly above the experimentally observed transition pressure range of 11.3 to 12.6 GPa. The HSE06 hybrid functional predicts a transition pressure of 12.4 GPa in excellent agreement with experiments. Exchange-correlation functionals using the local-density approximation and generalized-gradient approximations result in transition pressures ranging from 3.5 to 10.0 GPa, well below the experimental values. The transition pressure is sensitive to stress anisotropy. Anisotropy in the stress along any of the cubic axes of the diamond phase of silicon lowers the equilibrium transition pressure and may explain the discrepancy between the various experimental values as well as the small overestimate of the quantum Monte Carlo transition pressure.