Phase Transition Pathway Sampling via Swarm Intelligence and Graph Theory

TL;DR

This paper introduces PALLAS, a novel pathway sampling method combining swarm intelligence and graph theory, to predict solid-solid phase transition pathways without prior mechanistic assumptions, aiding materials design.

Contribution

The paper presents PALLAS, a new computational approach that efficiently finds low-energy phase transition pathways in solid-state systems, including novel pathways not previously observed.

Findings

Successfully identified known phase transition pathways in CdSe and Si.

Discovered a new lower-energy pathway for CdSe phase transition.

Provided detailed insights into the phase transition sequence during Si decompression.

Abstract

The prediction of reaction pathways for solid-solid transformations remains a key challenge. Here, we develop a pathway sampling method via swarm intelligence and graph theory, and demonstrate that our PALLAS method is an effective tool to help understand phase transformations in solid-state systems. The method is capable of finding low-energy transition pathways between two minima without having to specify any details of transition mechanism a priori. We benchmarked our PALLAS method against known phase transitions in cadmium selenide (CdSe) and silicon (Si). PALLAS readily identifies previously-reported, low-energy phase transition pathways for the wurtzite to rock-salt transition in CdSe and reveals a novel lower-energy pathway that has not yet been observed. In addition, PALLAS provides detailed information that explains the complex phase transition sequence observed during the decompression of Si from high pressure. Given the efficiency to identify low-barrier-energy reaction pathways, the PALLAS methodology represents a promising tool for materials by design with valuable insights for novel synthesis.