Nonseparable exchange–correlation functional for molecules, including homogeneous catalysis involving transition metals

• Published in: Physical chemistry chemical physics : PCCP Vol. 17; no. 18; pp. 12146 - 12160
• Main Authors: Yu, Haoyu S., Zhang, Wenjing, Verma, Pragya, He, Xiao, Truhlar, Donald G.
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 14.05.2015
• Subjects: Approximation; Bond energy; Borides; Catalysis; catalysis (heterogeneous); Exchange; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; materials and chemistry by design; Mathematical analysis; synthesis (novel materials); Training
• DOI: 10.1039/C5CP01425E
• ISSN: 1463-9076; 1463-9084

The goal of this work is to develop a gradient approximation to the exchange–correlation functional of Kohn–Sham density functional theory for treating molecular problems with a special emphasis on the prediction of quantities important for homogeneous catalysis and other molecular energetics. Our training and validation of exchange–correlation functionals is organized in terms of databases and subdatabases. The key properties required for homogeneous catalysis are main group bond energies (database MGBE137), transition metal bond energies (database TMBE32), reaction barrier heights (database BH76), and molecular structures (database MS10). We also consider 26 other databases, most of which are subdatabases of a newly extended broad database called Database 2015, which is presented in the present article and in its ESI. Based on the mathematical form of a nonseparable gradient approximation (NGA), as first employed in the N12 functional, we design a new functional by using Database 2015 and by adding smoothness constraints to the optimization of the functional. The resulting functional is called the gradient approximation for molecules, or GAM. The GAM functional gives better results for MGBE137, TMBE32, and BH76 than any available generalized gradient approximation (GGA) or than N12. The GAM functional also gives reasonable results for MS10 with an MUE of 0.018 Å. The GAM functional provides good results both within the training sets and outside the training sets. The convergence tests and the smooth curves of exchange–correlation enhancement factor as a function of the reduced density gradient show that the GAM functional is a smooth functional that should not lead to extra expense or instability in optimizations. NGAs, like GGAs, have the advantage over meta-GGAs and hybrid GGAs of respectively smaller grid-size requirements for integrations and lower costs for extended systems. These computational advantages combined with the relatively high accuracy for all the key properties needed for molecular catalysis make the GAM functional very promising for future applications.