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15 papers · 1 filter
Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
Flaviano Della Pia, Benjamin X. Shi, Venkat Kapil +3
As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for t…
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…
Hydration of NH in Water: Bifurcated Hydrogen Bonding Structures and Fast Rotational Dynamics
Jianqing Guo, Liying Zhou, Andrea Zen +5
Understanding the hydration and diffusion of ions in water at the molecular level is a topic of widespread importance. The ammonium ion (NH) is an exemplar system that has re…
An Accurate and Transferable Machine Learning Potential for Carbon
Patrick Rowe, Volker L Deringer, Piero Gasparotto +2
We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, nam…
TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo
Kousuke Nakano, Claudio Attaccalite, Matteo Barborini +9
TurboRVB is a computational package for {\it ab initio} Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code implements two types of well e…
Automated high-throughput Wannierisation
Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo +3
Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration a…