GridFF: Efficient Simulation of Organic Molecules on Rigid Substrates

TL;DR

GridFF is a fast and accurate method for simulating organic molecules on rigid surfaces, enabling efficient exploration of molecular configurations.

Contribution

GridFF introduces a novel approach using spatial grids and B-spline interpolation to drastically reduce computational costs while maintaining accuracy.

Findings

GridFF's CPU implementation achieves a 100–1000× speedup over all-atom simulations.

The GPU version samples millions of configurations per second, enabling exhaustive exploration of molecular configurations on surfaces.

GridFF can be extended to ab initio electron density-based potentials for enhanced accuracy.

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 100–1000×
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.

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