Artificial Neural Network-Based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes

• Published in: Journal of chemical theory and computation 2023-11, Vol.19 (21), p.7555-7566
• Main Authors: de Mendonça, João Paulo Almeida, Mariano, Lorenzo Antonio, Devijver, Emilie, Jakse, Noel, Poloni, Roberta
• Format: Article
• Language: English
• Subjects: Adiabatic flow; Artificial neural networks; Atomizing; Computational chemistry; Coordination compounds; Density functional theory; Machine learning; Neural networks; Particle swarm optimization; Physics; Quantum Electronic Structure; Transition metal compounds
• ISSN: 1549-9618; 1549-9626
• DOI: 10.1021/acs.jctc.3c00600

During the past decades, approximate Kohn–Sham density functional theory schemes have garnered many successes in computational chemistry and physics, yet the performance in the prediction of spin state energetics is often unsatisfactory. By means of a machine learning approach, an enhanced exchange and correlation functional is developed to describe adiabatic energy differences in transition metal complexes. The functional is based on the computationally efficient revision of the regularized, strongly constrained, and appropriately normed functional and improved by an artificial neural network correction trained over a small data set of electronic densities, atomization energies, and/or spin state energetics. The training process, performed using a bioinspired nongradient-based approach adapted for this work from the particle swarm optimization, is analyzed and discussed extensively. The resulting machine learned meta-generalized gradient approximation functional is shown to outperform most known density functionals in the prediction of adiabatic energy differences for a diverse set of transition metal complexes with varying local coordinations and metal choices.