ML for silicon and aluminosilicate atomistic simulations (MLSAAS)

École de Technologie Supérieure / Canada

Application Areas

Electronics
Energy
Transportation or construction

About

This MAP aims to develop a machine learning force field capable of describing interatomic forces with accuracy and modest computational cost. More specifically, a machine learning interatomic potential for clay mineral through their elemental constituent such as silicon and silica. Due to the complexity of clay systems, this research adopts a step-wise modelling approach in which elemental constituent of clay mineral are first considered.

Research purpose

Lab Equipment

AC Members

Claudiane
Ouellet-Plamondon
École de Technologie Supérieure

Publications

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Deadline to apply for our 2023 AC postdoc fellowship is Jan 27!

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