Low-density Phase Diagram of the Three-Dimensional Electron Gas

Phys. Rev. B 105, 245135 (2022)

Authors:

Sam Azadi, and N.D. Drummond

Abstract:

Variational and diffusion quantum Monte Carlo methods are employed to investigate the zero-temperature phase diagram of the three-dimensional homogeneous electron gas at very low density. Fermi fluid and body-centered cubic Wigner crystal ground state energies are determined using Slater-Jastrow-backflow and Slater-Jastrow many-body wave functions at different densities and spin polarizations in finite simulation cells. Finite-size errors are removed using twist-averaged boundary conditions and extrapolation of the energy per particle to the thermodynamic limit of infinite system size. Unlike previous studies, our results show that the electron gas undergoes a first-order quantum phase transition directly from a paramagnetic fluid to a body-centered cubic crystal at density parameter rs=86.6(7), with no region of stability for an itinerant ferromagnetic fluid. However there is a possible magnetic phase transition from an antiferromagnetic crystal to a ferromagnetic crystal at rs=93(3).

DQC: a Python program package for Differentiable Quantum Chemistry

Journal of Chemical Physics American Institute of Physics 156:8 (2022) 084801

Authors:

Muhammad Kasim, Susi Lehtola, Sam Vinko

Abstract:

Automatic differentiation represents a paradigm shift in scientific programming, where evaluating both functions and their derivatives is required for most applications. By removing the need to explicitly derive expressions for gradients, development times can be shortened and calculations can be simplified. For these reasons, automatic differentiation has fueled the rapid growth of a variety of sophisticated machine learning techniques over the past decade, but is now also increasingly showing its value to support ab initio simulations of quantum systems and enhance computational quantum chemistry. Here, we present an open-source differentiable quantum chemistry simulation code and explore applications facilitated by automatic differentiation: (1) calculating molecular perturbation properties, (2) reoptimizing a basis set for hydrocarbons, (3) checking the stability of self-consistent field wave functions, and (4) predicting molecular properties via alchemical perturbations.

Building high accuracy emulators for scientific simulations with deep neural architecture search

Machine Learning: Science and Technology IOP Science 3 (2021) 015013

Abstract:

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

A feasibility study of using X-ray Thomson Scattering to diagnose the in-flight plasma conditions of DT cryogenic implosions

ArXiv 2110.14361 (2021)

Authors:

H Poole, D Cao, R Epstein, I Golovkin, T Walton, SX Hu, M Kasim, SM Vinko, JR Rygg, VN Goncharov, G Gregori, SP Regan

DQC: a Python program package for Differentiable Quantum Chemistry

ArXiv 2110.11678 (2021)

Authors:

Muhammad F Kasim, Susi Lehtola, Sam M Vinko