NBO 7.0: New vistas in localized and delocalized chemical bonding theory

• Published in: Journal of computational chemistry 2019-09, Vol.40 (25), p.2234-2241
• Main Authors: Glendening, Eric D., Landis, Clark R., Weinhold, Frank
• Format: Article
• Language: English
• Subjects: Algorithms; chemical bonding theory; Chemical bonds; Chemical reactions; natural bond orbital; natural resonance theory; Organic chemistry; polyelectron population; density matrix; Resonance; wavefunction analysis
• ISSN: 0192-8651; 1096-987X
• DOI: 10.1002/jcc.25873

We briefly outline some leading features of the newest version, NBO 7.0, of the natural bond orbital (NBO) wavefunction analysis program. Major extensions include: (1) a new NPEPA module implementing Karafiloglou's “polyelectron population analysis” in the NBO framework; (2) new RDM2 program infrastructure for describing electron correlation effects based on full evaluation of the second‐order reduced density matrix; (3) improved convex‐solver implementation of natural resonance theory (NRT), allowing a greatly expanded range of applications and associated “resonance NBO” (RNBO) visualization of chemical reactivity; (4) a variety of other improvements in well‐established NBO algorithms. We also provide brief introduction to the new NBOPro@Jmol utility program, a plugin to the Jmol chemical structure viewer that serves as a convenient tool to provide on‐demand NBO descriptors or orbital visualizations for a broad variety of chemical inquiries in research or classroom applications. © 2019 Wiley Periodicals, Inc. NBO 7.0, the latest version of the natural bond orbital analysis program, significantly expands the range of chemical applications through (1) a vastly improved NRT solver and new “resonance NBO” (RNBO) basis type, (2) a full “natural” implementation (NPEPA) of Karafiloglou's polyelectron population analysis, (3) full Gaussian‐based implementation of natural energy decomposition analysis (NEDA), (4) expanded infrastructure for improved description of electron correlation effects with second‐order reduced density matrix (RDM2) evaluations, and (5) other algorithmic refinements to improve overall numerical stability and reliability. The authors also describe the new NBOPro@Jmol program, which supercedes previously available NBOView, NBOPro utilities for the common analysis, visualization, and data‐mining tasks of research and classroom applications.