GridFF: Efficient Simulation of Organic Molecules on Rigid Substrates

TL;DR

GridFF is a novel, efficient simulation method that uses spatial grids and interpolation to drastically speed up modeling molecules on rigid surfaces, enabling high-throughput surface science applications.

Contribution

We introduce GridFF, a fast, accurate grid-based approach for simulating molecules on substrates, significantly outperforming traditional atomistic models in speed.

Findings

100-1000x speedup over LAMMPS simulations

Millions of configurations sampled per second on GPU

Applicable to ab initio derived potentials for surface science

Abstract

We present GridFF, an efficient method for simulating molecules on rigid substrates, derived from techniques used in protein-ligand docking in biochemistry. By projecting molecule-substrate interactions onto precomputed spatial grids with tricubic B-spline interpolation, GridFF reduces the computational cost by orders of magnitude compared to traditional pairwise atomistic models, without compromising the accuracy of forces or trajectories. The CPU implementation of GridFF in the open-source FireCore package provides a 100-1000x speedup over all-atom simulations using LAMMPS, while the GPU implementation - running thousands of system replicas in parallel - samples millions of configurations per second, enabling an exhaustive exploration of the configuration space of small flexible molecules on surfaces within minutes. Furthermore, as demonstrated in our previous application of a similar technique to high-resolution scanning probe microscopy, GridFF can be extended beyond empirical pairwise potentials to those derived from ab initio electron densities. Altogether, this unlocks accurate high-throughput modeling of molecular self-assembly, adsorption, and scanning probe manipulation in surface science.