Machine Learning Frontier Orbital Energies of Nanodiamonds.

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology, and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new data set ND5k, consisting of 5089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory (DFTB) and their frontier orbital energies are computed using density functional theory (DFT) with the PBE0 hybrid functional. From this data set we derive a qualitative design suggestion for nanodiamonds in photocatalysis. We also compare recent machine learning models for predicting frontier orbital energies for similar structures as they have been trained on (interpolation on ND5k), and we test their abilities to extrapolate predictions to larger structures. For both the interpolation and extrapolation task, we find the best performance using the equivariant message passing neural network PaiNN. The second best results are achieved with a message passing neural network using a tailored set of atomic descriptors proposed here.

Citation

Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande, Frank Noé. Machine Learning Frontier Orbital Energies of Nanodiamonds. Journal of chemical theory and computation. 2023 Jul 25;19(14):4461-4473

PMID: 37053438

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