Systematic optimization of long-range corrected hybrid density functionals.
By Jeng-Da Chai, M. Head‐Gordon. 2008
3,455 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Organic Chemistry, Physical and Theoretical Chemistry
How researchers see this paper
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In Atomic and Molecular Physics, and Optics
- Roberto Peverati
- Donald G. Truhlar
- Olexandr Isayev
- Ester Livshits
- Ulrike Salzner
- Roi Baer
- Adrian E. Roitberg
- Christian Devereux
In Organic Chemistry
- Roberto Peverati
- Roi Baer
- Adrian E. Roitberg
- Justin S. Smith
- Donald G. Truhlar
- Olexandr Isayev
- Yan Zhao
- Stefan Grimme
In Physical and Theoretical Chemistry
- Roberto Peverati
- Ester Livshits
- Ulrike Salzner
- Roi Baer
- Adrian E. Roitberg
- Christian Devereux
- B. Nebgen
- Justin S. Smith
Citations in the corpus
- Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning (2019, methodology): “The existing ANI-1x active learning generated data set 25 is used to train an initial DFT (to the ω B97X/6-31G* model chemistry 63 ) potential, likewise dubbed ANI-1x.”
- Q‐Chem: an engine for innovation (2013, neutral): “Some of the most promising LRC functionals, which perform well for both ground-state 23 , 24 and excited-state 25 , 26 calculations, have been developed by the Q-Chem community (see Figure 3).”
- Tuned range-separated hybrids in density functional theory. (2010, background): “However, the potential curve is still spurious in terms of its asymptotic value and its dependence R (53, 141).”