We have developed a machine learning
(ML)-assisted Hybrid ReaxFF
simulation method (“Hybrid/Reax”), which alternates
reactive and non-reactive molecular dynamics simulations with the
assistance of ML models to simulate phenomena that require longer
time scales and/or larger systems than are typically accessible to
ReaxFF. Hybrid/Reax uses a specialized tracking tool during the reactive
simulations to further accelerate chemical reactions. Non-reactive
simulations are used to equilibrate the system after the reactive
simulation stage. ML models are used between reactive and non-reactive
stages to predict non-reactive force field parameters of the system
based on the updated bond topology. Hybrid/Reax simulation cycles
can be continued until the desired chemical reactions are observed.
As a case study, this method was used to study the cross-linking of
a polyethylene (PE) matrix analogue (decane) with the cross-linking
agent dicumyl peroxide (DCP). We were able to run relatively long
simulations [>20 million molecular dynamics (MD) steps] on a small
test system (4660 atoms) to simulate cross-linking reactions of PE
in the presence of DCP. Starting with 80 PE molecules, more than half
of them cross-linked by the end of the Hybrid/Reax cycles on a single
Xeon processor in under 48 h. This simulation would take approximately
1 month if run with pure ReaxFF MD on the same machine.