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SiO2 Fracture: Chemomechanics with a Machine Learning Hybrid QM/MM Scheme | by Argonne National Laboratory
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SiO2 Fracture: Chemomechanics with a Machine Learning Hybrid QM/MM Scheme

PI: James Kermode, University of Warwick

 

Understanding the chemo-mechanical phenomena that cause silicates to fracture would prove a great advantage to both enabling the process, as in large-scale mining, and eliminating it in products that rely on silicate materials. Researchers are utilizing advanced computing tools on Mira to better understand the behaviors that drive stress corrosion and chemically activated crack propagation at both the macro- and microscopic levels.

 

This image depicts the crack propagation in a two-dimensional bilayer of amorphous silicon dioxide modeled with a polarizable interatomic potential. Atoms are colored by their local potential energy on a scale normalized from dark blue, lowest energy, to yellow, highest energy.

 

Image credit: Marco Caccin and Alessandro De Vita, King’s College London; James Kermode, University of Warwick.

 

Scientific discipline: Materials Science: Condensed Matter and Materials

 

This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory.

 

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Uploaded on March 3, 2016