SKM 2023 – wissenschaftliches Programm
DY 57.4: Vortrag
Freitag, 31. März 2023, 10:15–10:30, ZEU 250
Automated chemical reaction network discovery for the simulation of long-timescale degradation of materials — •Joe Gilkes1, Mark Storr2, Reinhard J. Maurer1, and Scott Habershon1 — 1University of Warwick, United Kingdom — 2AWE plc, United Kingdom
Degradation of organic materials such as polymers occurs over time scales of years and involves rare reaction events over an expansive network of elementary processes. Building such networks in order to predict the degradation pathways of these materials requires tackling combinatorially large chemical spaces, and propagating these networks in time becomes considerably more difficult as network size increases. Predicting overall rates by which materials break down requires accurate calculations of the energetic barriers of thousands of elementary reaction steps, which also comes with a substantial computational cost. We present a software framework written in the Julia language for automatically traversing chemical reaction space with an approach that iteratively expands the reaction network through successive re-evaluation of degradation products. We couple this with a machine learning model to predict activation energies. The result is a workflow that can swiftly sample reaction space to create computationally efficient molecular breakdown networks, and then run simulations to predict the long-term stability of these species under a range of environmental conditions. We demonstrate this approach for the example of polyethylene degradation.
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