Deep Learning Metal Complex Properties with Natural Quantum Graphs

Hannes Kneiding, Ruslan Lukin, David Balcells (2022)
Highlighted by Jan Jensen

Figure 2 from the paper (c) The authors. Reproduced under the CC-BY-NC-ND 4.0 license

While there's been a huge amount of ML work on organic molecules, there as been comparatively little on trantition metal complexes (TMCs). One of the reasons is that many of the cheminformatics tools we take for granted are harder to apply to TMCs due to their more complex bonding situations. This makes bond perception and computing node-features like formal atomic charges, and hence graph representations, quite tricky. Which, in turn, makes standard ML tools like binary finger prints or graph-convolution NNs tricky to apply to TMCs.

This paper suggest using data from DFT/NBO calculations to create so-called "quantum graphs", where the edges are determined using both bonding orbitals and bond-orders while node- and edge-features are derived from other NBO properties.

This representation is combined with two graph-NN methods (MPNN and MXMNet) and trained against DFT properties such as the HOMO-LUMO gap. The results are quite good and generally better than radius graph methods such as SchNet. However, one should keep in mind that both the descriptors and properties are computed with DFT.

Given that the computational cost of the descriptors is basically the same as the property of interest, this is a proof-of-concept paper that shows the utility of the general idea. However, it remains to be seen whether cheaper descriptors (e.g. based on semi-empirical calculations) result in similar performance. However, given the current sparcity of ML tools for TMCs this is a very welcome advance.

This work is licensed under a Creative Commons Attribution 4.0 International License.