454 citations · 3.6k across the 53 of their papers we have counts for
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Condensed-Phase Quantum Chemistry
Paul J. Robinson, Adam Rettig, Hieu Q. Dinh +2
Molecular quantum chemistry has seen enormous progress in the last few decades thanks to the more advanced and sophisticated numerical techniques and computing power. Following the…
Stochastic resolution of identity to CC2 for large systems: excited state properties
Chongxiao Zhao, Qi Ou, Joonho Lee +1
We apply a stochastic resolution of identity approximation (sRI) to the CC2 method for excitation energy calculations. A set of stochastic orbitals are employed to decouple the cru…
Response properties in phaseless auxiliary field quantum Monte Carlo
Ankit Mahajan, Jo S. Kurian, Joonho Lee +2
We present a method for calculating first-order response properties in phaseless auxiliary field quantum Monte Carlo (AFQMC) through the application of automatic differentiation (A…
Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)
Michael S. Chen, Joonho Lee, Hong-Zhou Ye +3
Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we explo…
Twenty Years of Auxiliary-Field Quantum Monte Carlo in Quantum Chemistry: An Overview and Assessment on Main Group Chemistry and Bond-Breaking
Joonho Lee, Hung Q. Pham, David R. Reichman
In this work, we present an overview of the phaseless auxiliary-field quantum Monte Carlo (ph- AFQMC) approach from a computational quantum chemistry perspective, and present a num…
ipie: A Python-based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs
Fionn D. Malone, Ankit Mahajan, James S. Spencer +1
We report the development of a python-based auxiliary-field quantum Monte Carlo (AFQMC) program, ipie, with preliminary timing benchmarks and new AFQMC results on the isomerization…