The linear regression information-theoretic approach predicts electron correlation energies in molecular clusters, indicating effective methods for complex systems.
To employ some simple physics-inspired density-based information-theoretic approach (ITA) quantities to appreciate the electron correlation energies is an unaccomplished and ongoing task. In this work, we expand the territory of the LR(ITA) (LR means linear regression) protocol to more complex systems, including (i) 24 octane isomers; (ii) polymeric structures, polyyne, polyene, all-trans-polymethineimine, and acene; (iii) molecular clusters, such as metallic Ben and Mgn, covalent Sn, hydrogen-bonded protonated water clusters H+(H2O)n, and dispersion-bound carbon dioxide (CO2)n, and benzene (C6H6)n clusters. With LR(ITA), one can simply predict the post-Hartree‒Fock (such as MP2 and coupled cluster) electron correlation energies at the cost of Hartree‒Fock calculations, even with chemical accuracy. For large molecular clusters, we employ the linear-scaling generalized energy-based fragmentation (GEBF) method to gauge the accuracy of LR(ITA). Employing benzene clusters as an illustration, the LR(ITA) method shows similar accuracy to that of GEBF. Overall, we have verified that ITA quantities can be used to predict the electron correlation energies of various complex systems.
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Wang et al. (2025) studied this question.
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