Range-Separated Double-Hybrid Functional from Nonempirical Constraints
| Autor: | Juan Carlos Sancho-García, Carlo Adamo, Ángel J. Pérez-Jiménez, Éric Brémond, Marika Savarese |
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| Přispěvatelé: | Universidad de Alicante. Departamento de Química Física, Química Cuántica |
| Rok vydání: | 2018 |
| Předmět: |
Physics
Nonempirical constraints 010304 chemical physics Electronic correlation Double-hybrid functional Hydrogen atom 010402 general chemistry Range-separated model 01 natural sciences Dissociation (chemistry) 0104 chemical sciences Computer Science Applications Hybrid functional Ionization 0103 physical sciences Statistical physics Química Física Physical and Theoretical Chemistry Benchmark data Total energy |
| Zdroj: | RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) |
| Popis: | On the basis of our previous developments in the field of nonempirical double hybrids, we present here a new exchange-correlation functional based on a range-separated model for the exchange part and integrating a nonlocal perturbative correction to the electron correlation contribution. Named RSX-QIDH, the functional is free from any kind of empirical parametrization. Its range-separation parameter is set to recover the total energy of the hydrogen atom, thus eliminating the self-interaction error for this one-electron system. Subsequent tests on some relevant benchmark data sets confirm that the self-interaction error is particularly low for RSX-QIDH. This new functional provides also correct dissociation profiles for charged rare-gas dimers and very accurate ionization potentials directly from Kohn–Sham orbital energies. Above all, these good results are not obtained at the expense of other properties. Indeed, further tests on standard benchmarks show that RSX-QIDH is competitive with the more empirical ωB97X-2 double hybrid and outperforms the parent LC-PBE long-range corrected hybrid, thus underlining the important role of the nonlocal perturbative correlation. E.B. gratefully acknowledges the GENCI-CINES for HPC resources (Projects AP010810360 and A0040810359). |
| Databáze: | OpenAIRE |
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