Search

Predictive modeling of reaction equilibria presents one of the grand challenges in the field of molecular simulation. Difficulties in the study of such systems arise from the need (i) to accurately model both strong, short-ranged interactions leading to the formation of chemical bonds and weak interactions arising from the environment, and (ii) to sample the range of time scales involving frequent molecular collisions, slow diffusion, and infrequent reactive events. Here we present a novel reactive first-principles Monte Carlo (RxFPMC) approach that allows for investigation of reaction equilibria without the need to prespecify a set of chemical reactions and their ideal-gas equilibrium constants. We apply RxFPMC to investigate a nitrogen/oxygen mixture at T = 3000 K and p = 30 GPa, i.e., conditions that are present in atmospheric lightning strikes and explosions. The RxFPMC simulations show that the solvation environment leads to a significantly enhanced NO concentration that reaches a maximum when oxygen is present in slight excess. In addition, the RxFPMC simulations indicate the formation of NO[subscript 2] and N[subscript 2]O in mole fractions approaching 1%, whereas N[subscript 3] and O[subscript 3] are not observed. The equilibrium distributions obtained from the RxFPMC simulations agree well with those from a thermochemical computer code parametrized to experimental data., National Science Foundation (U.S.) (Grant CHE-1265849)

The dielectric properties of the hydrogen disordered hexagonal phase (Ih) of water ice have been computed using density functional theory (DFT) based Monte Carlo simulations in the isobaric isothermal ensemble. Temperature dependent data yield a fit for the Curie-Weiss law of the system and hence a prediction of the temperature of the phase transition from the Ih phase to the hydrogen ordered ice XI phase. Direct simulations around the phase transition temperature confirm and refine the predicted phase transition temperatures and provide data for further properties, such as the linear thermal expansion coefficient. Results have been obtained with both hybrid and semilocal density functionals, which yields insight in the performance of the electronic structure method. In particular, the hybrid functional yields significantly more realistic dielectric constants than the semilocal variant, namely epsilon approximate to 116 as opposed to epsilon approximate to 151 at 273 K (epsilon(experiment) = 95). This can be attributed to the tendency of semilocal functionals to be biased to configurations with a large dipole moment, and their overestimation of the dipole moments of these configurations. This is also reflected in the estimates of the Ih/XI transition temperature, which is 70-80 and 90-100 K for the hybrid and semilocal functional respectively. DFT based sampling of the millions of configurations necessary for this work has been enabled by a Tree Monte Carlo algorithm, designed for massively parallel computers.

MP2 provides a good description of hydrogen bonding in water clusters and includes long-range dispersion interactions without the need to introduce empirical elements in the description of the interatomic potential. To assess its performance for bulk liquid water under ambient conditions, an isobaric-isothermal (NpT) Monte Carlo simulation at the second-order Moller-Plesset perturbation theory level (MP2) has been performed. The obtained value of the water density is excellent (1.02 g/mL), and the calculated radial distribution functions are in fair agreement with experimental data. The MP2 results are compared to a few density functional approximations, including semilocal functionals, hybrid functionals, and functionals including empirical dispersion corrections. These results demonstrate the feasibility of directly sampling the potential energy surface of condensed-phase systems using correlated wave function theory, and their quality paves the way for further applications.

The design of modern parallel machines leads to powerful machines, but with complex architectures and hierarchical topologies. As a result, communication overheads associated with hardware asymmetry and interconnection network increase. In order to achieve scalable performances on these machines, it is essential to reduce communication costs on parallel applications such as CP2K. From computational chemistry domain, CP2K is a real-world parallel application that performs atomistic and molecular simulations. A linear-scaling density functional theory implementation based on an efficient sparse linear algebra kernel allows CP2K to simulate a million of atoms. Since this kernel is communication bound, the hardware asymmetry and interconnection network of current machines leads to a slowdown on CP2K performance for large number of processes. In this paper, we introduce a heuristic based process mapping to reduce the communication costs. It takes into account the machine topology and the sparse linear algebra kernel communication pattern to map processes over the machine. Results show that our process mapping provides on average a performance improvement of 50% when compared with the default implementation of CP2K.

Despite recent progress in linear scaling (LS) density function theory (DFT), the computational cost of the existing LS methods remains too high for a widespread adoption at present. In this work, we exploit nonorthogonal localized molecular orbitals to develop a series of LS methods for molecular systems with a low computational overhead. High efficiency of the proposed methods is achieved with a new robust two-stage variational procedure or by replacing the optimization altogether with an accurate nonself-consistent approach. We demonstrate that, even for challenging condensed-phase systems, the implemented LS methods are capable of extending the range of accurate DFT simulations to molecular systems that are an order of magnitude larger than those previously treated.

The second-order Meller-Plesset perturbation energy (MP2) and the Random Phase Approximation (RPA) correlation energy are increasingly popular post-Kohn Sham correlation methods. Here, a novel algorithm based on a hybrid Gaussian and Plane Waves (GPW) approach with the resolution-of-identity (RI) approximation is developed for MP2, scaled opposite-spin MP2 (SOS-MP2), and direct-RPA (dRPA) correlation energies of finite and extended system. The key feature of the method is that the three center electron repulsion integrals (mu nu broken vertical bar P) necessary for the RI approximation are computed by direct integration between the products of Gaussian basis functions mu nu and the electrostatic potential arising from the RI fitting densities P. The electrostatic potential is obtained in a plane waves basis set after solving the Poisson equation in Fourier space. This scheme is highly efficient for condensed phase systems and offers a particularly easy way for parallel implementation. The RI approximation allows to speed up the MP2 energy calculations by a factor 10 to 15 compared to the canonical implementation but still requires O(N-5) operations. On the other_ hand, the combination of RI with a Laplace approach in SOS similar to MP2 and an imaginary frequency integration in dRPA reduces the computational effort to O(N-4) in both cases. In addition to that, our implementations have low memory requirements and display excellent parallel scalability up to tens of thousands of processes. Furthermore, exploiting graphics processing units (GPU), a further speedup by a factor similar to 2 is observed compared to the standard only CPU implementations. In this way, RI-MP2, RI-SOS-M132, and RI-dRPA calculations for condensed phase systems containing hundreds of atoms and thousands of basis functions can be performed within minutes employing a few hundred hybrid nodes. In order to validate the presented methods, various molecular crystals have been employed as benchmark sys