Exploiting Locality in Full Configuration Interaction Quantum Monte Carlo for Fast Excitation Generation
Oskar Weser, Ali Alavi, Giovanni Li Manni · 2023
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Description
Oskar Weser, Ali Alavi, and Giovanni Li Manni present a faster computing method for quantum chemistry in a 2023 paper. Their method samples random paths to solve how electrons arrange in a molecule, but picking the next step can be slow. The new approach uses the fact that nearby orbitals matter most when orbitals stay local.
They extend an older precomputed sampling trick so double steps are drawn more wisely, and add a new way to draw single steps unevenly. Tests cover a chain of hydrogen atoms, stacked benzene rings, and an iron based model. Speed gains ran from twofold to fourfold, judged by lower noise for the same computing time.
Catalog record
| CREATOR | Oskar Weser, Ali Alavi, Giovanni Li Manni |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 74 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |