9 citations · 35 across the 11 of their papers we have counts for
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Fast Evaluation of Unbiased Atomic Forces in ab initio Variational Monte Carlo via the Lagrangian Technique
Kousuke Nakano, Stefano Battaglia, Jürg Hutter
Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of th…
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…
A Denser Hydrogen Inferred from First-Principles Simulations Challenges Jupiter's Interior Models
Cesare Cozza, Kousuke Nakano, Saburo Howard +3
First-principle modeling of dense hydrogen is crucial in materials and planetary sciences. Despite its apparent simplicity, predicting the ionic and electronic structure of hydroge…