Frontiers of Stochastic Electronic Structure Calculations

The past decade has witnessed a rapid growth in the development of stochastic electronic structure methods. Stochastic methods encompass a broad range of techniques to either treat high dimensionality or accelerate algorithms normally implemented in a deterministic style. For example, quantum Monte Carlo (QMC) techniques may sample over real space positions, determinants, or diagrams in order to incorporate electron correlation effects in a scalable way. These methods have recently been applied to strongly correlated materials and have achieved high accuracy. Stochastic algorithms have also been used to improve the scaling of methods such as density functional theory, Green function techniques, or MP2, allowing for application to much larger systems than otherwise available. Finally, the flexibility of stochastic algorithms has enabled the application of machine learning algorithms to many-body wave functions, a topic of considerable interest currently. The articles in this special issue will address recent algorithmic developments as well as applications of stochastic electronic structure methods.

Topics covered include, but are not limited to:

Guest Editors

Kenneth Jordan, University of Pittsburgh

Miguel Morales, Lawrence Livermore National Laboratory

Luke Shulenburger, Sandia National Laboratories

Lucas Wagner, University of Illinois at Urbana-Campaign

JCP Associate Editors

David Sherill, Georgia Institute of Technology

Todd J. Martínez, Stanford University

David Reichman, Columbia University