ML for silicon and aluminosilicate atomistic simulations (MLSAAS)

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.