The State of Exascale Quantum Monte Carlo

Ab initio Quantum Monte Carlo (QMC) methods currently offer the highest accuracy achievable for general materials. However, systematic developments are needed to extend their reach in terms of scale (atom and electron count), complexity of materials and physics, range of properties that can be computed and compared with experiment, and to address fundamental approximations such as the fixed-node approximation and Fermion sign problem. The combination of recently improved methods and algorithms plus increases in computation power promises to extend their reach to materials from across the entire periodic table and to enable the few underlying approximations to be systematically tested. I will present the current computational performance and scientific capabilities of real space QMC methods as implemented in the open source QMCPACK code[1,2]. Portability to and performance of Intel, AMD, and NVIDIA GPUs is achieved by using a careful software design[3] and state of the art software development approaches. Examples will be given of application to quantum materials such as TbMn₆Sn₆, MnBi₂Te₄, and 2D bilayers, where the combinations of electron correlation, charge, spin, and lattice couplings challenges more approximate electronic structure approaches and use of QMC is strongly merited. Finally, I will give an outlook of where progress is critically needed in the field.

1. P. R. C. Kent et al J. Chem. Phys. 152 174105 (2020), doi: 10.1063/5.0004860

2. J. Kim et al. J. Phys.: Condens. Matter 30 195901 (2018) doi: 10.1088/1361-648X/aab9c3

3. Y. Luo, P. Doak, and P. Kent, IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing (HiPar) (2022) 22 doi: 10.1109/HiPar56574.2022.00008