9 citations · 35 across the 11 of their papers we have counts for
12 papers
jQMC: A JAX-based ab initio quantum Monte Carlo package designed for GPU-accelerated computing
Kousuke Nakano, Michele Casula
We present jQMC, a Python-based computational package for {\it ab initio} Quantum Monte Carlo (QMC) simulations, designed for modern GPU-accelerated computing environments. jQMC im…
The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project
Qiming Sun, Matthew R Hermes, Xiaojie Wu +100
Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum…
Assessing the impact of nodal surface optimization in fixed-node diffusion Monte Carlo on non-covalent interactions
Kousuke Nakano, Benjamin X. Shi, Dario Alfè +1
Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely-employed benchmark methods for noncovalent interactions (NCIs). However, recent studies have rep…
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…
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…
Beyond single-reference fixed-node approximation in ab initio Diffusion Monte Carlo using antisymmetrized geminal power applied to systems with hundreds of electrons
Kousuke Nakano, Sandro Sorella, Dario Alfè +1
Diffusion Monte Carlo (DMC) is an exact technique to project out the ground state (GS) of a Hamiltonian. Since the GS is always bosonic, in fermionic systems the projection needs t…
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…