Welcome to our TurboRVB website¶
- TurboRVB is now an open-source project!
- A. Raghav et al. have published a paper in J. Chem. Theory Comput. 19, 2222-2229 (2023).
- E. Posenitskiy et al. have published a paper in J. Chem. Phys. 158, 174801 (2023).
- L. Monacelli et al. have published a paper in Nat. Phys. 19, 845–850 (2023).
- A. Tirelli et al. have published a paper in Phys. Rev. B, 106, L041105 (2022).
- K. Nakano et al. have published a paper in J. Chem. Phys. 156, 034101 (2022).
- K. Nakano et al. have published a paper in Phys. Rev. B 103, L121110 (2021).This paper has been selected as an Editors’ Suggestion.
TurboRVB is a computational package for ab initio Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code was initially launched by Prof. Sandro Sorella and Prof. Michele Casula and has been continuously developed by many contributors for over 20 years. The code implements two types of well established QMC algorithms: Variational Monte Carlo (VMC), and Diffusion Monte Carlo in its robust and efficient lattice regularized variant (LRDMC).
The source codes of TurboRVB and other associated packages are available from Source codes.
TurboRVB is distinguishable from other QMC codes in the following features:
The code employs a resonating valence bond (RVB)-type wave function, such as the Jastrow Geminal/Jastrow Pfaffian. This wave function includes the correlation effect beyond the Jastrow-Slater wave function, which is commonly used in other QMC codes.
Implemented state-of-art optimization algorithms, such as the stochastic reconfiguration and the linear method, realize a stable optimization of the amplitude and nodal surface of many-body wave functions at the variational quantum Monte Carlo level.
The code implements the so-called lattice regularized diffusion Monte Carlo method, which provides a numerically stable diffusion quantum Monte Carlo calculation.
The implementation of an adjoint algorithmic differentiation allows us to differentiate many-body wave functions efficiently and to perform structural optimizations and calculate molecular dynamics.