I will present recent developments in predicting electronic excitations using the combination of stochastic computational techniques and many-body theory. This methodology is based on diagrammatic methods; it employs real-time and real-space sampling combined with (i) decomposition of operators via random vectors and (ii) recasting expectation values as statistical estimators. This formalism leads to substantial computational savings and reduced scaling with the number of electrons. It enables first-principles predictions of quasiparticle energies in systems with thousands of atoms. In detail, I will describe our recent work on simulating nanoscale condensed systems.[2,3,4] Further, I will show that statistical sampling of interactions is an efficient route to tackle complex excitations that require consideration of quasiparticle-quasiparticle interactions.[5] I will demonstrate that these higher-order effects are crucial for the description of charge addition, improve the quasiparticle energies, and capture multi-quasiparticle excitations otherwise missing in more conventional approaches.[5,6] Despite the calculations' complexity, the stochastic many-body techniques exhibit linear scaling with the system size.

Stochastic methods for predicting quasiparticle excitations and their interactions

ACS Spring Meeting 2021

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