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GPU implementation of the diffusion monte carlo algorithm for use with PhysNet interatomic potentials

                   Diffusion Monte Carlo Code for PhysNet
                     Meuwly Group, University of Basel                  

The Diffusion Monte Carlo (DMC) Code here can be used to calculate the vibrational zero-point energy of molecular systems. It is based on the unbiased DMC algorithm detailed in Ref [1] and uses PhysNet [2] based potential energy surfaces (PESs) to obtain energies for molecular geometries. It is recommended to run the calculations on a GPU for higher efficiency.

The following installation steps were tested on a Ubuntu 18.04 workstation and using Conda 4.12.0 (see https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)

a) Create an environment named (e.g.) physnet_dmc_env, install Python 3.6:

conda create --name physnet_dmc_env python=3.6

Activate it:

conda activate physnet_dmc_env

(deactivating it by typing: conda deactivate)

b) install tensorflow (and all dependencies) using conda:

    conda install tensorflow-gpu==1.13.1

if a compatible gpu is available, else try:

    conda install tensorflow==1.13.1

The use of the code is illustrated based on the formic acid monomer molecule, for which a PhysNet PES has been published [3] (also see https://github.com/MMunibas/PhysNet-formic-acid-PES).

The repository contains the following files and folders:

i) dmc_physnet_main.py: Contains the DMC code

ii) fam_cart_coor.xyz: Contains the equilibrium FAM geometry and a reference geometry

iii) run_fam_cart.inp: Contains the DMC simulation settings and NN architecture used

iv) tl_models: Contains the NN models that are transfer learnt to the CCSD(T)/aVTZ level of theory

v) neural_network: Contains the PhysNet implementation

The code can be run by invoking:

python dmc_physnet_main.py @run_fam_cart.inp -i fam_cart_coor.xyz

The following files are produced by the DMC code:

i) fam_cart_coor.log: Summarizes the DMC settings and gives the final result of the simulation (ZPE of FAM on the PES is around 7320 cm**-1, see Ref. [3])

ii) fam_cart_coor.pot: Keeps track of the reference energy and number of alive walkers throughout the simulation.

iii) configs_fam_cart_coor.xyz: Saves the walkers from the last 10 DMC steps for visualization purposes (in .xyz format)

iv) defective_fam_cart_coor.xyz: Saves walkers that are defective, i.e. from regions on the PES that were insufficiently sampled (holes).

When using the PhysNet PES for FAM and the DMC code, please cite the following papers:

Unke, O. T. and Meuwly, M "PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges", J. Chem. Theory Comput. 2019, 15, 6, 3678–3693

Käser, S. and Meuwly, M. "Transfer learned potential energy surfaces: accurate anharmonic vibrational dynamics and dissociation energies for the formic acid monomer and dimer", Phys. Chem. Chem. Phys., 2022, 24, 5269-5281.

[1] Ioan Kosztin, Byron Faber, and Klaus Schulten; Am. J. Phys. 64, 633 (1996); https://doi.org/10.1119/1.18168

[2] Oliver T. Unke and Markus Meuwly; J. Chem. Theory Comput. 2019, 15, 6, 3678–3693

[3] Käser, S. and Meuwly, M.; Phys. Chem. Chem. Phys., 2022, 24, 5269-5281.

If you have any questions about the PES free to contact Silvan Kaeser ([email protected])