40 citations · 68 across the 5 of their papers we have counts for
5 papers
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…
Efficient calculation of unbiased atomic forces in ab initio Variational Monte Carlo
Kousuke Nakano, Michele Casula, Giacomo Tenti
Ab initio quantum Monte Carlo (QMC) is a state-of-the-art numerical approach for evaluating accurate expectation values of many-body wavefunctions. However, one of the major drawba…
Principal deuterium Hugoniot via Quantum Monte Carlo and -learning
Giacomo Tenti, Kousuke Nakano, Andrea Tirelli +2
We present a study of the principal deuterium Hugoniot for pressures up to GPa, using Machine Learning potentials (MLPs) trained with Quantum Monte Carlo (QMC) energies, forc…
High pressure hydrogen by machine learning and quantum Monte Carlo
Andrea Tirelli, Giacomo Tenti, Kousuke Nakano +1
We have developed a technique combining the accuracy of quantum Monte Carlo in describing the electron correlation with the efficiency of a Machine Learning Potential (MLP). We use…