Singling Out Dynamic and Nondynamic Correlation

Via-Nadal, Mireia; Rodríguez‐Mayorga, Mauricio; Ramos‐Cordoba, Eloy; Matito, Eduard

doi:10.1021/acs.jpclett.9b01376

Cited by 38 publications

(48 citation statements)

References 41 publications

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“…While numerous MR diagnostics have been developed [54][55][56][57][58][59][60][61][62][63][64][65] , we focus on diagnostics [63][64][65] based on fractional occupation numbers (FONs) that can be obtained from computationally affordable finite-temperature DFT 66 (FT-DFT). Since electron correlation can be attributed alternately to both dynamical and non-dynamical (i.e., MR) contributions [67][68][69] , Matito and coworkers [63][64] have derived expressions for dynamical, I D , and non-dynamical, I ND , quantities.…”

Section: Toc Graphicsmentioning

confidence: 99%

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Liu

Duan

Kulik

2020

J. Phys. Chem. Lett.

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Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3d transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO-LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO-LUMO gaps and FON-based diagnostics reveals differences in metal-and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO-LUMO gap complexes while ensuring low MR character. File list (4) download file view on ChemRxiv MRML3_v12.pdf (2.13 MiB) download file view on ChemRxiv A_MRML3.png (241.62 KiB) download file view on ChemRxiv SI_MRML3_v8.pdf (7.74 MiB) download file view on ChemRxiv SI_MRML3_Data-07242020.zip (93.84 MiB)

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Section: Toc Graphicsmentioning

confidence: 99%

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Liu

Duan

Kulik

2020

J. Phys. Chem. Lett.

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mentioning

confidence: 99%

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

Liu¹,

Duan²,

Kulik

2020

Preprint

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<p>Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as those containing open-shell, 3<i>d</i> transition metals that can be expected to have strong multi-reference (MR) character. For discovery workflows to be predictive, we need automated, low-cost methods that can distinguish the regions of chemical space where DFT should be applied from those where it should not. We curate over 4,800 open-shell transition-metal complexes up to hundreds of atoms in size from prior high-throughput DFT studies and evaluate affordable, finite-temperature DFT evaluation of fractional occupation number (FON)-based MR diagnostics. We show that intuitive measures of strong correlation (i.e., the HOMO–LUMO gap) are not predictive of MR character as judged by FON-based diagnostics. Analysis of independently trained machine learning (ML) models to predict HOMO–LUMO gaps and FON-based diagnostics reveals differences in metal- and ligand-sensitivity of the two quantities. We use our trained ML models to rapidly evaluate MR character over a space of ca. 187,000 theoretical complexes, identifying large-scale trends in spin-state-dependent MR character and finding small HOMO–LUMO gap complexes while ensuring low MR character. </p>

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“…For all CCSD and MP2 wavefunctions, we have computed the I ND index 103,104 obtained from the range-separation partition of the Coulomb hole. 105,106 I ND is proportional to the deviation from idempotency of the first-order reduced density matrix. 107,108 Unlike T1 and D1 diagnostics, I ND can be calculated for any wavefunction from natural orbital occupations.…”

Section: Methodsmentioning

confidence: 99%

How Many Electrons Holds a Molecular Electride?

Sitkiewicz¹,

Ramos‐Cordoba²,

Luis³

et al. 2021

Preprint

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<div> <div> <div> <p>Electrides are very peculiar ionic compounds where electrons occupy the anionic positions. In a crystal lattice, these isolated electrons often group forming channels or surfaces, furnishing electrides with a plethora of traits with promising technological applications. Despite their huge potential, thus far, only a few stable electrides have been produced because of the intricate synthesis they entail. Due to the difficulty in assessing the presence of isolated electrons, the characterization of electrides also poses some serious challenges. In fact, their properties are expected to depend on the arrangement of these electrons in the molecule. Among the criteria that we can use to characterize electrides, the presence of a non-nuclear attractor (NNA) of the electron density is both the rarest and the most salient feature. Therefore, a correct description of the NNA is crucial to determine the properties of electrides. In this paper, we analyze the NNA and the surrounding region of nine molecular electrides with the goal of determining the number of isolated electrons that are held in the electride. We have seen that the correct description of a molecular electride hinges on the electronic structure method employed for the analyses. In particular, one should employ a basis set with sufficient flexibility to describe the region close to the NNA and a density functional approximation that does not suffer from large delocalization errors. Finally, we have classified these nine molecular electrides according to the most likely number of electrons that we can find in the NNA. We believe this classification highlights the strength of the electride character and will prove useful in the design of new electrides.</p> </div> </div> </div>

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Singling Out Dynamic and Nondynamic Correlation

Cited by 38 publications

References 41 publications

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Rapid Detection of Strong Correlation with Machine Learning for Transition Metal Complex High-Throughput Screening

How Many Electrons Holds a Molecular Electride?

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