.. _noncovalent_interactions:

Non-covalent interactions
=========================

Purpose
-------
This benchmark tests if the **MLIP** can reproduce interaction energies of molecular complexes driven by non-covalent interactions.
Non-covalent interactions are of highest importance for the structure and function of every biological molecule. This benchmark
assesses a broad range of interaction types: London dispersion, hydrogen bonds, ionic hydrogen bonds, repulsive contacts and sigma
hole interactions.


Description
-----------
The benchmark leverages the `mlip <https://github.com/instadeepai/mlip>`_ library for model energy inference on all structures
corresponding to the distance scans of bi-molecular complexes in the dataset. The key metric is the **RMSE of the interaction energy**,
which is the minimum of the energy well in the distance scan, relative to the energy of the dissociated complex, compared to the **QM** reference data.
For repulsive contacts, the maximum of the energy profile is used instead.

Note that some of the molecular complexes in the benchmark dataset contain exotic elements (see *dataset section*).
If the benchmarked **MLIP** cannot run an element of a molecular complex, the complex will simply be skipped.

.. list-table::
   :widths: 25 45
   :header-rows: 0

   * - .. figure:: img/butadiene_diazomethane.png
          :width: 100%
          :align: center
          :figclass: align-center

     - .. figure:: img/pes.png
          :width: 100%
          :align: center
          :figclass: align-center

Dataset
-------
This benchmark uses the datasets from the `NCI Atlas <http://www.nciatlas.org/>`_, with dissociation energy profiles.
These datasets contain **QM** optimized geometries, along with **CCSD(T)/CBS** level interaction energies. The molecular complexes of
these datasets contain typical organic small molecules, but also more exotic species and elements. Here is a summary of the
datasets used in this benchmark:

.. list-table:: NCI Atlas Datasets
   :widths: 20 30 50
   :header-rows: 1

   * - Dataset Name
     - Type of interaction
     - Subsets
   * - D442x10
     - London dispersion
     - Noble Gases, Boron, HCNO, Halogens
   * - HB375x10
     - Hydrogen bonds
     - OH-N, OH-O, OH-C, NH-N, NH-O, …
   * - HB300SPXx10
     - Hydrogen bonds extended to S, P and halogens
     - XH-S, XH-P, XH-Cl, XH-Br
   * - IHB100x10
     - Ionic hydrogen bonds
     - O, N, C with cationic donors and anionic acceptors
   * - R739x5
     - Repulsive contacts
     - HCNO, halogens, PS
   * - SH250x10
     - Sigma hole interactions
     - P, S, Br, Cl, I

Interpretation
--------------
The **RMSE** of the interaction energies should be **as low as possible**. This metric is likely to be very different for the different
interaction types and data subsets. The **RMSE** in interaction error **should be compared per interaction type** and then in a more
fine-grained visualization for the data subsets to identify areas of weakness for the **MLIP**. Within these areas of weakness,
individual dissociation energy profiles can be visually inspected to see how they compare to the reference.