Open-source development experiences in scientific software: the HANDE quantum Monte Carlo project

The HANDE quantum Monte Carlo project offers accessible stochastic algorithms for general use for scientists in the field of quantum chemistry. HANDE is an ambitious and general high-performance code developed by a geographically-dispersed team with a variety of backgrounds in computational science. In the course of preparing a public, open-source release, we have taken this opportunity to step back and look at what we have done and what we hope to do in the future. We pay particular attention to development processes, the approach taken to train students joining the project, and how a flat hierarchical structure aids communicationComment: 6 pages. Submission to WSSSPE

Accurate exchange-correlation energies for the warm dense electron gas

Density matrix quantum Monte Carlo (DMQMC) is used to sample exact-on-average $N$-body density matrices for uniform electron gas systems of up to 10$^{124}$ matrix elements via a stochastic solution of the Bloch equation. The results of these calculations resolve a current debate over the accuracy of the data used to parametrize finite-temperature density functionals. Exchange-correlation energies calculated using the real-space restricted path-integral formalism and the $k$-space configuration path-integral formalism disagree by up to $\sim$$10$\% at certain reduced temperatures $T/T_F \le 0.5$ and densities $r_s \le 1$. Our calculations confirm the accuracy of the configuration path-integral Monte Carlo results available at high density and bridge the gap to lower densities, providing trustworthy data in the regime typical of planetary interiors and solids subject to laser irradiation. We demonstrate that DMQMC can calculate free energies directly and present exact free energies for $T/T_F \ge 1$ and $r_s \le 2$.Comment: Accepted version: added free energy data and restructured text. Now includes supplementary materia

Preconditioning and perturbative estimators in full configuration interaction quantum Monte Carlo

We propose the use of preconditioning in FCIQMC which, in combination with perturbative estimators, greatly increases the efficiency of the algorithm. The use of preconditioning allows a time step close to unity to be used (without time-step errors), provided that multiple spawning attempts are made per walker. We show that this approach substantially reduces statistical noise on perturbative corrections to initiator error, which improve the accuracy of FCIQMC but which can suffer from significant noise in the original scheme. Therefore, the use of preconditioning and perturbatively-corrected estimators in combination leads to a significantly more efficient algorithm. In addition, a simpler approach to sampling variational and perturbative estimators in FCIQMC is presented, which also allows the variance of the energy to be calculated. These developments are investigated and applied to benzene (30e,108o), an example where accurate treatment is not possible with the original method.Comment: 15 pages, 7 figure

The sign problem and population dynamics in the full configuration interaction quantum Monte Carlo method

The recently proposed full configuration interaction quantum Monte Carlo method allows access to essentially exact ground-state energies of systems of interacting fermions substantially larger than previously tractable without knowledge of the nodal structure of the ground-state wave function. We investigate the nature of the sign problem in this method and how its severity depends on the system studied. We explain how cancelation of the positive and negative particles sampling the wave function ensures convergence to a stochastic representation of the many-fermion ground state and accounts for the characteristic population dynamics observed in simulations.Comment: 11 pages. 6 figure

Reconstruction of three-dimensional porous media using generative adversarial neural networks

To evaluate the variability of multi-phase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a novel method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image datasets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that GANs can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.Comment: 21 pages, 20 figure

The Isolation of a New S-Methyl Benzothioate Compound from a Marine-Derived Streptomyces sp.

The application of an HPLC bioactivity profiling/microtiter plate technique in conjunction with microprobe NMR instrumentation and access to the AntiMarin database has led to the isolation of a new 1. In this example, 1 was isolated from a cytotoxic fraction of an extract obtained from marine-derived Streptomyces sp. cultured on Starch Casein Agar (SCA) medium. The 1D and 2D 1H NMR and ESIMS data obtained from 20 μg of compound 1 fully defined the structure. The known 2 was also isolated and readily dereplicated using this approach

Discovery of a White Dwarf Companion to HD 159062

We report on the discovery of a white dwarf companion to the nearby late G dwarf star, HD 159062. The companion is detected in 14 years of precise radial velocity (RV) data, and in high-resolution imaging observations. RVs of HD 159062 from 2003-2018 reveal an acceleration of $-13.3\pm0.12\ \rm{m s}^{-1}$, indicating that it hosts a companion with a long-period orbit. Subsequent imaging observations with the ShaneAO system on the Lick Observatory 3-meter Shane telescope, the PHARO AO system on the Palomar Observatory 5-meter telescope, and the NIRC2 AO system at the Keck II 10-meter telescope reveal a faint companion 2.7'' from the primary star. We performed relative photometry, finding $\Delta J = 10.09 \pm 0.38$ magnitudes, $\Delta K_{S} = 10.06 \pm 0.22$ magnitudes, and $\Delta L' = 9.67\pm0.08$ magnitudes for the companion from these observations. Analysis of the radial velocities, astrometry, and photometry reveals that the combined data set can only be reconciled for the scenario where HD 159062 B is a white dwarf. A full Bayesian analysis of the RV and imaging data to obtain the cooling age, mass, and orbital parameters of the white dwarf indicates that the companion is an old $M_{B} = 0.65^{+0.12}_{-0.04} M_{\odot}$ white dwarf with an orbital period of $P = 250^{+130}_{-76}$ years, and a cooling age of $\tau = 8.2^{+0.3}_{-0.5}$ Gyr.Comment: 10 pages, 9 figure

Nonequilibrium dynamics of fully frustrated Ising models at T=0

We consider two fully frustrated Ising models: the antiferromagnetic triangular model in a field of strength, $h=H T k_B$, as well as the Villain model on the square lattice. After a quench from a disordered initial state to T=0 we study the nonequilibrium dynamics of both models by Monte Carlo simulations. In a finite system of linear size, $L$, we define and measure sample dependent "first passage time", $t_r$, which is the number of Monte Carlo steps until the energy is relaxed to the ground-state value. The distribution of $t_r$, in particular its mean value, $$, is shown to obey the scaling relation, $\sim L^2 \ln(L/L_0)$, for both models. Scaling of the autocorrelation function of the antiferromagnetic triangular model is shown to involve logarithmic corrections, both at H=0 and at the field-induced Kosterlitz-Thouless transition, however the autocorrelation exponent is found to be $H$ dependent.Comment: 7 pages, 8 figure