Prediction of orthorhombic lattice constants using machine learning

A crystal structure is composed of a unit cell repeating itself to occupy a space, forming what is known as a lattice. This arrangement is dictated by the structure’s lattice constants. Lattice constants are integral for investigation into the properties of crystal materials. However, current methods to determine such constants may be computationally exhaustive and time consuming. In this study, we utilized a random forest machine learning model to predict the lattice constants of orthorhombic crystal structures. This model was trained using the various materials’ structural properties.