Year: 2026
Type: preprint
Abstract: Predicting the atomic structure of large systems requires simultaneous exploration of atomic positions, species, and, generally, the number of atoms. The combinatorial growth of possible arrangements can make this search difficult even for standard global optimization methods. We introduce Hereditary Effective Atom Theory (HEAT), which integrates Effective Atom Theory (EAT) into a genetic algorithm. HEAT combines EAT's continuous relaxation of atomic positions, species, and population with the global search capabilities of genetic algorithms. Central to the method is a crossover scheme that directly superimposes two parent structures and relaxes the combined structure. HEAT outperforms conventional genetic-algorithm baselines in recovering a missing 240-atom H sublattice, predicting a 364-atom edge-dislocation core, and reconstructing a 1150-atom interior region of twisted-bilayer $\mathrm{WSe_2}$ from experimental ptychographic constraints. These results demonstrate how continuous effective-atom relaxation improves the exploration of large combinatorial structure spaces.
Source: arXiv (Cornell University)
Institution: Cornell University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: Center for Bright Beams, G7328236249
OpenAlex ID: W7223206929