Rowan currently supports neural-network-potential (NNP), semiempirical, density-functional-theory (DFT), and Hartree–Fock (HF) calculations. In incorporating density functionals, we have attempted to balance including useful functionals with a desire to avoid overwhelming end users with unnecessary options. If a certain functional that's not included is exceedingly important to your work, please let us know!
Rowan supports all commonly used classes of functional, including meta-GGA functionals and range-separated hybrids, although not every engine supports every functional. For advice on which method to choose for a given task, see our recommendations below. You can find benchmarks for many of these methods on our dedicated benchmarks site.
For instructions on how to select methods when submitting calculations using the Python API, see the API documentation.
Rowan supports Hartree–Fock calculations. For open-shell systems, Rowan uses the unrestricted Hartree–Fock formalism.
Sure and Grimme's HF-3c method is supported as well.
| Keywords |
|---|
hf |
hf_3c |
Rowan supports a variety of density functionals.
For a variety of historical reasons, there are many competing implementations of several popular density functionals, like B3LYP and PBE, which can lead to slight differences when comparing outputs of one program to another. (See this excellent overview by Susi Lehtola and Miguel Marques, the authors of Libxc.)
Here's a quick overview of all the functionals that Rowan supports:
| Name | Keyword | Class | Hybridicity | %HF exchange | Double-hybrid Nature |
|---|---|---|---|---|---|
| PBE | pbe | GGA | |||
| BP86 | bp86 | GGA | |||
| B97-3c | b97_3c | GGA | |||
| B97-D3BJ | b97_d3bj | GGA | |||
| r2SCAN | r2scan | mGGA | |||
| r2SCAN-3c | r2scan_3c | mGGA | |||
| TPSS | tpss | mGGA | |||
| M06-L | m06l | mGGA | |||
| PBE0 | pbe0 | GGA | Global | 25% | |
| B3LYP | b3lyp | GGA | Global | 20% | |
| TPSSh | tpssh | mGGA | Global | 10% | |
| M06 | m06 | mGGA | Global | 27% | |
| M06-2X | m062x | mGGA | Global | 54% | |
| CAM-B3LYP | camb3lyp | GGA | Range-separated | 19-65% | |
| ωB97X-D3 | wb97x_d3 | GGA | Range-separated | 20-100% | |
| ωB97X-V | wb97x_v | GGA | Range-separated | 17-100% | |
| ωB97X-3c | wb97x_3c | GGA | Range-separated | 17-100% | |
| ωB97M-V | wb97m_v | mGGA | Range-separated | 15-100% | |
| ωB97M-D3BJ | wb97m_d3bj | mGGA | Range-separated | 15-100% | |
| DSD-BLYP-D3BJ | dsd_blyp_d3bj | GGA | Double-hybrid | 69% | Spin-component-scaled |
| Skala | skala | mGGA | Neural network |
For a more in-depth look at the differences between these methods, see our article about the "Charlotte's Web" of DFT.
The 1996 Perdew–Burke–Ernzerhof functional.
| Keyword |
|---|
pbe |
Becke's 1988 exchange functional with Perdew's 1988 correlation functional.
| Keyword |
|---|
bp86 |
Brandenburg, Bannwarth, Hansen, and Grimme's 2018 revised composite variant of Becke's 1997 power-series ansatz, using the D3 dispersion correction and a modified version of the def2-TZVP basis set, called "mTZVP".
| Keyword |
|---|
b97_3c |
Grimme's 2011 reparameterization of Becke's 1997 power-series ansatz, using the D3 dispersion correction and Becke and Johnson (BJ) damping function.
| Keyword |
|---|
b97_d3bj |
Furness and Sun's 2020 improvement over the numerically unstable SCAN functional. r2SCAN still struggles with numerical instability, as shown by Lehtola and Marques recently.
| Keyword |
|---|
r2scan |
Grimme's 2021 composite variant of Furness and Sun's r2SCAN, using a new mTZVPP basis set.
| Keyword |
|---|
r2scan_3c |
Scuseria and Perdew's 2003 mGGA functional, with no empirical parameters.
| Keyword |
|---|
tpss |
Zhao and Truhlar's 2006 local mGGA functional.
| Keyword |
|---|
m06l |
Adamo and Barone's hybrid functional derived from PBE (also evaluated by Ernzerhof and Scuseria).
| Keyword |
|---|
pbe0 |
The famous 1994 functional of Stephens, Devlin, Chabalowski, and Frisch. (We follow the original Gaussian implementation here in employing the VWN(RPA) correlation functional rather than the VWN5 correlation functional.)
| Keyword |
|---|
b3lyp |
Scuseria and Perdew's 2003 one-paramter global hybrid version of their TPSS mGGA functional.
| Keyword |
|---|
tpssh |
Zhao and Truhlar's 2007 hybrid functional, based on their 2006 M06-L local functional.
| Keyword |
|---|
m06 |
Zhao and Truhlar's 2007 hybrid functional with double the HF exchange of M06, based on their 2006 M06-L local functional.
| Keyword |
|---|
m062x |
Yanai, Tew, and Handy's 2004 range-separated hybrid based on B3LYP.
| Keyword |
|---|
camb3lyp |
Chai's reparameterization of ωB97X-D with the D3 dispersion correction.
| Keyword |
|---|
wb97x_d3 |
Mardirossian and Head-Gordon's 2014 10-parameter combinatorially optimized GGA functional, with the VV10 nonlocal dispersion correction.
| Keyword |
|---|
wb97x_v |
Müller, Hansen, and Grimme's composite modification of ωB97X-V using a vDZP basis set, a specially adapted D4 dispersion correction, and large-core effective core potentials.
| Keyword |
|---|
wb97x_3c |
Mardirossian and Head-Gordon's 2016 12-parameter combinatorially optimized mGGA functional, with the VV10 nonlocal dispersion correction. Consistently one of the most accurate non-double hybrid functionals out there: see e.g. this benchmark and this one.
| Keyword |
|---|
wb97m_v |
Najibi and Goerigk's reparameterization of ωB97M-V for use with the D3 dispersion correction and Becke and Johnson (BJ) damping function.
| Keyword |
|---|
wb97m_d3bj |
Kozuch, Gruzman, and Martin's 2010 general-purpose GGA-based double hybrid with spin-component-scaled MP2 correlation and empirical dispersion.
| Keyword |
|---|
dsd_blyp_d3bj |
Luise et al.'s 2026 machine-learned exchange-correlation functional with no Hartree–Fock exchange.
| Keyword |
|---|
skala |
Spicher and Grimme's 2020 geometry-based forcefield method, using a simplified D4 dispersion model and the extended tight-binding (xTB) framework.
| Keyword |
|---|
gfn_ff |
| Keyword |
|---|
gfn0_xtb |
gfn1_xtb |
gfn2_xtb |
Froitzheim, Müller, Hansen, and Grimme's 2025 general-purpose semiempirical electronic structure method, using the xTB framework.
| Keyword |
|---|
g_xtb |
| Keyword |
|---|
pm6 |
pm6_d3h4x |
pm6_org |
pm7 |
Rowan currently supports a variety of neural network potentials (NNPs), each with its own strengths.
Our benchmarks site compares NNPs, other low-cost methods, and standard DFT functionals against coupled-cluster references and experimental data to help give a sense of each method's performance.
Anstine, Zubatyuk, and Isayev's 2024 potential for closed-shell organic species. Trained to reproduce calculations at the ωB97M-D3/def2-TZVPP level of theory.
| Name | Keyword |
|---|---|
| AIMNet2 (ωB97M-D3) | aimnet2_wb97md3 |
Levine et al.'s 2025 potential trained on (and released with) the Open Molecules 2025 (OMol25) dataset using the equivariant smooth energy network (eSEN) architecture, with conservative force predictions. Trained to reproduce calculations at the ωB97M-V/def2-TZVPD level of theory.
| Name | Keyword |
|---|---|
| OMol25's eSEN Conserving Small | omol25_conserving_s |
| Name | Keyword |
|---|---|
| Orb-v3 (Conservative Inf. OMat) | orb_v3_conservative_inf_omat |
| OrbMol | orb_v3_conservative_omol |
Wood et al.'s 2025 UMA is a model from Meta FAIR's chemistry team that spans many domains of chemistry and materials science. The UMA family of models were trained on the OMol25, OMat24, OC20, ODAC25, and OMC25 datasets.
UMA comprises five output tasks corresponding to different training datasets. While the UMA models were trained on all datasets, outputs differ per task. The choice of output task is important and should be matched to the scientific domain. Rowan lists three tasks:
omol: the task corresponding to the Open Molecules 2025 (OMol25) dataset of molecular systems. Trained to reproduce calculations at the ωB97M-V/def2-TZVPD level of theory.omat: the task corresponding to the Open Materials 2024 (OMat24) dataset of materials. Trained to reproduce calculations at the PBE(+U)/PAW (VASP, plane-wave basis) level of theory.omc: the task corresponding to the Open Molecular Crystals 2025 (OMC25) dataset of molecular crystals. Trained to reproduce calculations at the PBE-D3/PAW (VASP, plane-wave basis) level of theory.Rowan supports the UMA Small, UMA Small 1.2 (the latest version), and UMA Medium models.
| Name | Keywords |
|---|---|
| UMA Small (OMol) | uma_s_omol |
| UMA Small (OMat) | uma_s_omat |
| UMA Small (OMC) | uma_s_omc |
| UMA Small 1.2 (OMol) | uma_s_1_2_omol |
| UMA Small 1.2 (OMat) | uma_s_1_2_omat |
| UMA Small 1.2 (OMC) | uma_s_1_2_omc |
| UMA Medium (OMol) | uma_m_omol |
| UMA Medium (OMat) | uma_m_omat |
| UMA Medium (OMC) | uma_m_omc |
Choosing the appropriate level of theory can be challenging! An extensive 2011 Grimme benchmark suggests that B97-D3 performs best among conventional "pure" density functionals, while higher accuracy can be achieved using any of the hybrid functionals included in Rowan. This recent paper from Grimme and co-workers offers many useful recommendations depending on the task at hand. The best guide, however, is to find a paper which reports benchmark results for systems like those you wish to study and follow those recommendations.
We recommend using a low-cost method like UMA Small 1.2, GFN2-xTB, or g-xTB for as much preliminary work as possible. These methods approach range-separated hybrid DFT accuracy at a fraction of the cost, enabling much faster iteration in early stages of modeling projects.