Rapid and Accurate Prediction of the Axial Magnetic Anisotropy in Cobalt(II) Complexes Using a Machine-Learning Approach.
Henrique C Silva JuniorHeloisa N S MenezesGlaucio B FerreiraGuilherme P GuedesPublished in: Inorganic chemistry (2023)
Estimating the magnetic anisotropy for single-ion magnets is complex due to its multireference nature. This study demonstrates that deep neural networks (DNNs) can provide accurate axial magnetic anisotropy ( D ) values, closely matching the complete-active-space self-consistent-field (CASSCF) quality using density functional theory (DFT) data. We curated an 86-parameter database (UFF1) with electronic data from over 33000 cobalt(II) compounds. The DNN achieved an R 2 of 0.906 and a mean absolute error of 18.1 cm -1 in comparison to reference CASSCF D values. Remarkably, it is 11 times more accurate than DFT methods and 7700 times faster. This approach hints at DNNs predicting the anisotropy in larger molecules, even when trained on smaller ligands.
Keyphrases
- density functional theory
- neural network
- molecular dynamics
- molecularly imprinted
- machine learning
- big data
- high resolution
- electronic health record
- artificial intelligence
- molecular docking
- reduced graphene oxide
- resistance training
- metal organic framework
- emergency department
- carbon nanotubes
- gold nanoparticles
- body composition
- data analysis
- high intensity
- simultaneous determination