1. Machine Learning-Assisted Hybrid ReaxFF Simulations
- Author
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Adri C. T. van Duin, Dundar E. Yilmaz, and Woodward William H H
- Subjects
Materials science ,Xeon ,business.industry ,Dicumyl peroxide ,Decane ,Machine learning ,computer.software_genre ,Force field (chemistry) ,Computer Science Applications ,chemistry.chemical_compound ,Molecular dynamics ,chemistry ,Molecule ,Artificial intelligence ,Physical and Theoretical Chemistry ,ReaxFF ,business ,computer ,Topology (chemistry) - Abstract
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.
- Published
- 2021