D. Graf

most citedTranslational eigenstates of He@C

_{60}

from four-dimensional \textit{ab initio} Potential Energy Surfaces interpolated using Gaussian Process Regression

3 citations · 3 across the 2 of their papers we have counts for

collaborators

3 papers

physics.chem-ph2025

Application of Correlated-Wavefunction and Density-Functional Theories to Endofullerenes: A Cautionary Tale

K. Panchagnula, D. Graf, K. R. Bryenton +3

A recent study by Panchagnula et al. [J. Chem. Phys. 161, 054308 (2024)] illustrated the non-concordance of a variety of electronic structure methods at describing the symmetric do…

physics.chem-ph2024

Targeting spectroscopic accuracy for dispersion bound systems from ab initio techniques: translational eigenstates of Ne@C $_{70}$

K. Panchagnula, D. Graf, E. R. Johnson +1

We investigate the endofullerene system Ne@C $_{70}$ , by constructing a three-dimensional Potential Energy Surface (PES) describing the translational motion of the Ne atom. We compa…

physics.chem-ph2024★ 3 cited

Translational eigenstates of He@C $_{60}$ from four-dimensional \textit{ab initio} Potential Energy Surfaces interpolated using Gaussian Process Regression

K. Panchagnula, D. Graf, F. E. A. Albertani +1

We investigate the endofullerene system $^{3}$ He@C $_{60}$ with a four-dimensional Potential Energy Surface (PES) to include the three He translational degrees of freedom and C $_{60}$ …

Application of Correlated-Wavefunction and Density-Functional Theories to Endofullerenes: A Cautionary Tale

Targeting spectroscopic accuracy for dispersion bound systems from ab initio techniques: translational eigenstates of Ne@C70​

Translational eigenstates of He@C60​ from four-dimensional \textit{ab initio} Potential Energy Surfaces interpolated using Gaussian Process Regression

Targeting spectroscopic accuracy for dispersion bound systems from ab initio techniques: translational eigenstates of Ne@C $_{70}$

Translational eigenstates of He@C $_{60}$ from four-dimensional \textit{ab initio} Potential Energy Surfaces interpolated using Gaussian Process Regression