Insight Into Packing Structures as well as Electrical States for Ti Nanoparticles on Heating Through Integrating Machine Learning Potential With a MD/QM Hybrid Strategy

This study employs an integrated multiscale simulation strategy that combines machine learning potential, molecular dynamics, and density‐functional tight‐binding methods to investigate the temperature‐ and size‐dependent structural transitions and electronic state evolution in titanium nanoparticles (2.2–4.4 nm). The aim is to provide atomic‐level insight into the relationship between structural rearrangements and electronic properties during thermal processes. Analysis of packing patterns reveals distinct structural transformation pathways across different particle sizes. The potential energy and its difference reveal a transition temperature range (400–1450 K) that marks significant changes in atomic thermal vibration, strongly influenced by particle size. Fermi energy and the energy gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital illustrate key electronic behavior trends, showing a decrease in the energy gap with increasing temperature and particle size, eventually approaching zero for larger nanoparticles, indicating enhanced metallic character. Mulliken charge distributions further exhibit a characteristic core‐shell spatial pattern, while charge density differences emphasize surface electron depletion and internal charge redistribution, which vary systematically with nanoparticle size.