Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials

Understand

Machine learning of atomic-scale properties is revolutionizing molecular modelling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a fraction of the costs.

• The accuracy, speed and reliability of machine-learning potentials, however, depends strongly on the way atomic configurations are represented, i.e.
• the choice of descriptors used as input for the machine learning method.
• The raw Cartesian coordinates are typically transformed in "fingerprints", or "symmetry functions", that are designed to encode, in addition to the structure, important properties of the potential-energy surface like its invariances with respect to rotation, translation and permutation of like atoms.