Geometry-Based Neural-Network Prediction of Electron Localization Function Topology in Dense Hydrogen
This paper presents a machine-learning framework that accurately predicts the electron localization function topology of dense hydrogen directly from atomic geometry, demonstrating high fidelity across fluid and crystalline phases while bypassing explicit electronic-structure calculations.