Efficient Machine Learning Configuration Interaction for Bond Breaking Problems-Reference-Cited by-同舟云学术

Efficient Machine Learning Configuration Interaction for Bond Breaking Problems

Published:2023-04-16 Issue:16 Volume:127 Page:3705-3713
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Rano Madhumita¹^ORCID,Ghosh Debashree¹^ORCID

Affiliation:

1. School of Chemical Sciences, Indian Association for the Cultivation of Science, 2A & 2B, Raja Subodh Chandra Mallick Road, Jadavpur, Kolkata 700032, India

Funder

Science and Engineering Research Board

Council for Scientific and Industrial Research

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.2c09103

Reference43 articles.

1. Identification of deadwood in configuration spaces through general direct configuration interaction

2. Incremental treatments of the full configuration interaction problem

3. A complete active space SCF method (CASSCF) using a density matrix formulated super-CI approach

4. Second-order perturbation theory with a CASSCF reference function

5. Second-order perturbation theory with a density matrix renormalization group self-consistent field reference function: Theory and application to the study of chromium dimer

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