5 papers
Load-Balanced Diffusion Monte Carlo Method with Lattice Regularization
Kousuke Nakano, Sandro Sorella, Michele Casula
Ab initio quantum Monte Carlo (QMC) is a stochastic approach for solving the many-body Schrödinger equation without resorting to one-body approximations. QMC algorithms are readily…
Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo
Giacomo Tenti, Kousuke Nakano, Michele Casula
Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on ti…
Hydrogen liquid-liquid transition from first principles and machine learning
Giacomo Tenti, Bastian Jäckl, Kousuke Nakano +2
The molecular-to-atomic liquid-liquid transition (LLT) in high-pressure hydrogen is a fundamental topic touching domains from planetary science to materials modeling. Yet, the natu…
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…
Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions
Kousuke Nakano, Benjamin X. Shi, Dario Alfè +1
Basis set incompleteness error (BSIE) is a common source of error in quantum chemistry (QC) calculations, but it has not been comprehensively studied in fixed-node Diffusion Monte…