19 citations · 26 across the 4 of their papers we have counts for
4 papers
Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test
Alfonso Annarelli, Emiel Slootman, Claudia Filippi
We introduce quantum Monte Carlo (QMC)-trained multi-state machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the…
QMCkl: A Kernel Library for Quantum Monte Carlo Applications
Emiel Slootman, Vijay Gopal Chilkuri, Aurelien Delval +16
Quantum Monte Carlo (QMC) methods deliver highly accurate electronic structure calculations but are computationally intensive. The quantum Monte Carlo kernel library (QMCkl) provid…
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
Flaviano Della Pia, Benjamin X. Shi, Yasmine S. Al-Hamdani +30
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and mole…
Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
Emiel Slootman, Igor Poltavsky, Ravindra Shinde +4
Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces f…