Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials

Understand

Modeling non-empirical and highly flexible interatomic potential energy surfaces (PES) using machine learning (ML) approaches is becoming popular in molecular and materials research.

• Training an ML-PES is typically performed in two stages: feature extraction and structure-property relationship modeling.
• The feature extraction stage transforms atomic positions into a symmetry-invariant mathematical representation.
• This representation can be fine-tuned by adjusting on a set of so-called "hyper-parameters" (HPs).