Modeling Fe(II) Complexes Using Neural Networks

Abstract

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.

Simulating Metal-Imidazole Complexes

“…However, reproducing metal ions’ structural features and thermodynamic properties in water or protein systems is still challenging for these methods under the prerequisite of maintaining a low computational cost and being physically meaningful. Apart from the accurate machine learning models, − some commonly used physically meaningful force field models include but are not limited to bonded models, nonbonded models, Drude oscillators models, , cationic dummy atom (CDA) and CDA pol models, , and the ReaxFF model …”