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Toward Linear Scaling Auxiliary-Field Quantum Monte Carlo with Local Natural Orbitals.
Kurian, Jo S; Ye, Hong-Zhou; Mahajan, Ankit; Berkelbach, Timothy C; Sharma, Sandeep.
Afiliação
  • Kurian JS; Department of Chemistry, University of Colorado, Boulder, Colorado 80302, United States.
  • Ye HZ; Department of Chemistry, Columbia University, New York, New York 10027, United States.
  • Mahajan A; Department of Chemistry, University of Colorado, Boulder, Colorado 80302, United States.
  • Berkelbach TC; Department of Chemistry, Columbia University, New York, New York 10027, United States.
  • Sharma S; Department of Chemistry, Columbia University, New York, New York 10027, United States.
J Chem Theory Comput ; 20(1): 134-142, 2024 Jan 09.
Article em En | MEDLINE | ID: mdl-38113195
ABSTRACT
We develop a local correlation variant of auxiliary-field quantum Monte Carlo (AFQMC) based on local natural orbitals (LNO-AFQMC). In LNO-AFQMC, independent AFQMC calculations are performed for each localized occupied orbital using a truncated set of tailored orbitals. Because the size of this space does not grow with the system size for a target accuracy, the method has linear scaling. Applying LNO-AFQMC to molecular problems containing a few hundred to a thousand orbitals, we demonstrate convergence of total energies with significantly reduced costs. The savings are more significant for larger systems and larger basis sets. However, even for our smallest system studied, we find that LNO-AFQMC is cheaper than canonical AFQMC, in contrast with many other reduced-scaling methods. Perhaps most significantly, we show that energy differences converge much more quickly than total energies, making the method ideal for applications in chemistry and material science. Our work paves the way for linear scaling AFQMC calculations of strongly correlated systems, which would have a transformative effect on ab initio quantum chemistry.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article