From the 1 of 9 linked papers with an AI index.
9 papers
jQMC: A JAX-based ab initio quantum Monte Carlo package designed for GPU-accelerated computing
Kousuke Nakano, Michele Casula
The paper introduces jQMC, a Python/JAX‑based software package for performing ab initio Quantum Monte Carlo simulations on modern GPU‑accelerated hardware, supporting VMC and latti…
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…
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…
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…