posted on 2024-03-05, 09:22authored byHongni Jin, Kenneth M. Merz
We report a Fe(II)
data set of more than 23000 conformers in both
low-spin (LS) and high-spin (HS) states. This data set was generated
to develop a neural network model that is capable of predicting the
energy and the energy splitting as a function of the conformation
of a Fe(II) organometallic complex. In order to achieve this, we propose
a type of scaled electronic embedding to cover the long-range interactions
implicitly in our neural network describing the Fe(II) organometallic
complexes. For the total energy prediction, the lowest MAE is 0.037
eV, while the lowest MAE of the splitting energy is 0.030 eV. Compared
to baseline models, which only incorporate short-range interactions,
our scaled electronic embeddings improve the accuracy by over 70%
for the prediction of the total energy and the splitting energy. With
regard to semiempirical methods, our proposed models reduce the MAE,
with respect to these methods, by 2 orders of magnitude.