Continuous Representation of Alchemical Degrees of Freedom in Machine Learning Interatomic Potentials

This work introduces a method to access the alchemical degrees of freedom inherent in machine learning interatomic potentials, enabling smooth interpolation between different compositional states of materials and efficient calculation of alchemical gradients.