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47 changes: 47 additions & 0 deletions docs/source/user_guide/benchmarks/surfaces.rst
Original file line number Diff line number Diff line change
Expand Up @@ -102,6 +102,7 @@ Reference data:
* Same as input data
* PBE-D3(BJ), MPRelaxSet settings


Elemental Slab Oxygen Adsorption
================================

Expand Down Expand Up @@ -145,3 +146,49 @@ Reference data:
* S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. Chevrier, K. A. Persson, G. Ceder, "Python Materials Genomics (pymatgen): A Robust, Open-Source Python Library for Materials Analysis," Comput. Mater. Sci., 2013, 68, 314–319. https://doi.org/10.1016/j.commatsci.2012.10.028

* Tran et al. relaxed the slabs using spin-polarized PBE calculations performed in VASP, with a cutoff energy of 400 eV.


Graphene Wetting Under Strain
=============================

Summary
-------

Performance in predicting adsorption energies for a water molecule on graphene under varying strain conditions.

Metrics
-------

MAE of adsorption energies

For each combination of water molecule orientation, water-graphene distance, and strain
condition, the adsorption energy is calculated by taking the difference between the
energy of the combined water + graphene system and the sum of individual water and
graphene energies. This is compared to the reference adsorption energy, calculated in the
same way.

MAE of binding energies & lengths

The adsorption energies calculated above are fitted to Morse potentials, to obtain an
effective binding energy and binding length (i.e. minimum of adsorption energy curve) for
each strain condition. This is compared to the reference binding energy & length,
calculated in the same way.

Computational cost
------------------

Very low: tests are likely to take less than a minute to run on CPU.

Data availability
-----------------

Input data:

* Structures were taken from:

* D. W. Lim, X. R. Advincula, W. C. Witt, F. L. Thiemann, C. Schran, “Revealing Strain Effects on the Graphene-Water Contact Angle Using a Machine Learning Potential,” *awaiting publication* (arXiv:2601.20134)

Reference data:

* Same as input data
* PBE (with D3 dispersion correction), FHI-aims "intermediate" settings
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