Identification of general linear relationships between activation energies and enthalpy changes for dissociation reactions at surfaces

Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity

The complex interaction between molecules and catalyst surfaces leads to great difficulties in understanding and predicting the activity and selectivity in heterogeneous catalysis. Here we develop an end-to-end artificial intelligence framework for the activity prediction of heterogeneous catalytic systems (AI-Cat method), which takes simple inputs from names of molecules and metal catalysts and outputs the reaction energy profile from the input molecule to low energy pathway products. The AI-Cat method combines two neural network models, one for predicting reaction patterns and the other for providing the reaction barrier and energy, with a Monte Carlo tree search to resolve the low energy pathways in a reaction network. We then apply AI-Cat to resolve the reaction network of glycerol hydrogenolysis on Cu surfaces, which is a typical selective C-O bond activation system and of key significance for biomass-derived polyol utilization. We show that glycerol hydrogenolysis features a huge reaction network of relevant candidates, containing 420 reaction intermediates and 2467 elementary reactions. Among them, the surface-mediated enol-keto tautomeric resonance is a key step to facilitate the primary C-OH bond breaking and thus selects 1,2-propanediol as the major product on Cu catalysts. 1,3-Propanediol can only be produced under strong acidic conditions and high surface H coverage by following a hydrogenation-dehydration pathway. AI-Cat further discovers six low-energy reaction patterns for C-O bond activation on metals that is of general significance to polyol catalysis. Our results demonstrate that the reaction prediction for complex heterogeneous catalysis is now feasible with AI-based atomic simulation and a Monte Carlo tree search.

A unique Co@CoO catalyst for hydrogenolysis of biomass-derived 5-hydroxymethylfurfural to 2,5-dimethylfuran

The development of precious-metal-free catalysts to promote the sustainable production of fuels and chemicals from biomass remains an important and challenging target. Here, we report the efficient hydrogenolysis of biomass-derived 5-hydroxymethylfurfural to 2,5-dimethylfuran over a unique core-shell structured catalyst, Co@CoO that affords the highest productivity among all catalysts, including noble-metal-based catalysts, reported to date. Surprisingly, we find that the catalytically active sites reside on the shell of CoO with oxygen vacancies rather than the metallic Co. The combination of various spectroscopic experiments and computational modelling reveals that the CoO shell incorporating oxygen vacancies not only drives the heterolytic cleavage, but also the homolytic cleavage of H2 to yield more active Hδ- species, resulting in the exceptional catalytic activity. Co@CoO also exhibits excellent activity toward the direct hydrodeoxygenation of lignin model compounds. This study unlocks, for the first time, the potential of simple metal-oxide-based catalysts for the hydrodeoxygenation of renewable biomass to chemical feedstocks.

A Universal Descriptor for Complicated Interfacial Effects on Electrochemical Reduction Reactions

Supported catalysts have exhibited excellent performance in various reactions. However, the rational design of supported catalysts with high activity and certain selectivity remains a great challenge because of the complicated interfacial effects. Using recently emerged two-dimensional materials supported dual-atom catalysts (DACs@2D) as a prototype, we propose a simple and universal descriptor based on inherent atomic properties (electronegativity, electron type, and number), which can well evaluate the complicated interfacial effects on the electrochemical reduction reactions (i.e., CO₂, O₂, and N₂ reduction reactions). Based on this descriptor, activity and selectivity trends in CO₂ reduction reaction are successfully elucidated, in good agreement with available experimental data. Moreover, several potential catalysts with superior activity and selectivity for target products are predicted, such as CuCr/g-C₃N₄ for CH₄ and CuSn/N-BN for HCOOH. More importantly, this descriptor can also be extended to evaluate the activity of DACs@2D for O₂ and N₂ reduction reactions, with very small errors between the prediction and reported experimental/computational results. This work provides feasible principles for the rational design of advanced electrocatalysts and the construction of universal descriptors based on inherent atomic properties.

Towards the rational design of Pt-based alloy catalysts for the low-temperature water-gas shift reaction: from extended surfaces to single atom alloys

The rational design of Pt-based catalysts for the low-temperature water-gas-shift (LT-WGS) reaction is an active research field because of its important role played in the fuel cell-based hydrogen economy, especially in mobile applications. Previous theoretical analyses have suggested that Pt alloys, leading to a weaker CO binding affinity than the Pt metal, could help alleviate CO poisoning and thus should be promising catalysts of the LT-WGS reaction. However, experimental research along this line was rather ineffective in the past decade. In the present work, we employed the state-of-the-art kinetic Monte Carlo (KMC) simulations to examine the influences of the electronic effect by introducing sub-surface alloys and/or core–shell structures, and the synergetic effect by introducing single atom alloys on the catalytic performance of Pt-alloy catalysts. Our KMC simulations have highlighted the importance of the OH binding affinity on the catalyst surfaces to reduce the barrier of water dissociation as the rate determining step, instead of the CO binding affinity as has been emphasized before in conventional mean-field kinetic models. Along this new direction of catalyst design, we found that Pt–Ru synergetic effects can significantly increase the activity of the Pt metal, leading to Ru_1–3@Pt alloys with a tetrahedron site of one surface-three subsurface Ru atoms on the Pt host, showing a turnover frequency of about five orders of magnitude higher than the Pt metal.

KMC simulations show that decreasing the barrier of H₂O decomposition is more beneficial than decreasing the CO binding affinity in LT-WGS, while the latter was overemphasized by MF-MKM. Here Ru_1–3@Pt alloy is proposed as a promising catalyst.

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.

CO2—A Crisis or Novel Functionalization Opportunity?

The growing emission of carbon dioxide (CO2), combined with its ecotoxicity, is the reason for the intensification of research on the new technology of CO2 management. Currently, it is believed that it is not possible to eliminate whole CO2 emissions. However, a sustainable balance sheet is possible. The solution is technologies that use carbon dioxide as a raw material. Many of these methods are based on CO2 methanation, for example, projects such as Power-to-Gas, production of fuels, or polymers. This article presents the concept of using CO2 as a raw material, the catalytic conversion of carbon dioxide to methane, and consideration on CO2 methanation catalysts and their design.

Insights into reaction mechanisms of ethanol electrooxidation at the Pt/Au(111) interfaces using density functional theory

Understanding ethanol electrooxidation reaction kinetics is fundamental to the development of direct ethanol fuel cells.

Structure-Reactivity Relationship for Nano-Catalysts in the Hydrogenation/Dehydrogenation Controlled Reaction Systems

For the activity of a nano-catalyst, a general and quantitative solution to building direct structure-reactivity relationship has not yet been established. On top of the first-principle-based kinetic Monte Carlo (KMC) simulations, we developed a model to build the adsorption site dependence of the activity. We applied this model to study the nano effects of Cu catalysts in the water-gas shift reaction. By accumulating the activities of different adsorption sites, our model satisfactorily reproduced the experimental apparent activation energies for catalysts with sizes over hundreds of nanometers, which were out of reach for conventional KMC simulations. Our results disclose that, even for a cubic catalyst with size of 877 nm, its activity can still be closely related to the activity of edge sites, instead of only the exposed Cu(100) facets as might be expected. The present model is expected to be useful for systems that are controlled by the hydrogenation/dehydrogenation processes.

Two-dimensional ferroelectric metal for electrocatalysis

The coexistence of metallicity and ferroelectricity has been an intriguing and controversial phenomenon as these two material properties are considered incompatible in bulk. We clarify the concept of the ferroelectric metal by revisiting the original definitions for ferroelectric and metal. Two-dimensional (2D) ferroelectrics with out-of-plane polarization can be engineered via layer stacking to a genuine ferroelectric metal characterized by switchable polarization and non-zero density of states at the Fermi level. We demonstrate that 2D ferroelectric metals can serve as electrically-tunable, high-quality electrocatalysts.

Key to C2 production: selective C-C coupling for electrochemical CO2 reduction on copper alloy surfaces

The C-C coupling kinetic variations are observed on Cu alloys with Pt, Pd, or Au surface sites. The OC-COH coupling is kinetically more favorable than OC-CHO coupling, which originates from increased reactivity of adsorbed *CO species. Linear energy relations for C-C association/dissociation could simplify the energetic evaluation for C₂ production.

Towards the object-oriented design of active hydrogen evolution catalysts on single-atom alloys

Given a desired property, locating relevant materials is always highly desired but very challenging in a range of areas, including heterogeneous catalysis. Obviously, object-oriented design/screening is an ideal solution to this problem. Herein, we develop an inverse catalyst design workflow in Python (CATIDPy) that utilizes a genetic-algorithm-based global optimization method to guide on-the-fly density functional theory calculations, successfully realizing the highly accelerated location of active single-atom alloy (SAA) catalysts for the hydrogen evolution reaction (HER). 70 binary and 752 ternary SAA candidate catalysts are identified for the HER. Furthermore, via considering the segregation stability and cost of materials, we extracted 6 binary and 142 ternary SAA candidate catalysts that are recommended for experimental synthesis. Remarkably, guided by these theoretical identifications, homogeneously dispersed Ni-based bimetallic catalysts (e.g., NiMo, NiAl, Ni₃Al, NiGa, and NiIn) were synthesized experimentally to test the reliability of the CATIDPy workflow, and they showed superior HER performance to bare Ni foam, indicating huge potential for use in real-world water electrolysis techniques. Perhaps more importantly, these results demonstrate the capacity of such a proposed approach for investigating unexplored chemical spaces to efficiently design promising catalysts without knowledge from the expert domain, which has far-reaching implications.

An inverse catalyst design workflow in Python (CATIDPy) for discovering unexplored chemical spaces successfully realized the highly accelerated location of active single-atom alloy (SAA) catalysts for the hydrogen evolution reaction (HER).

Exploring the Dominant Role of Atomic- and Nano-Ruthenium as Active Sites for Hydrogen Evolution Reaction in Both Acidic and Alkaline Media

Ru nanoparticles (NPs) and single atoms (SAs)-based materials have been investigated as alternative electrocatalysts to Pt/C for hydrogen evolution reaction (HER). Exploring the dominant role of atomic- and nano-ruthenium as active sites in acidic and alkaline media is very necessary for optimizing the performance. Herein, an electrocatalyst containing both Ru SAs and NPs anchored on defective carbon (RuSA+NP /DC) has been synthesized via a Ru-alginate metal-organic supramolecules conversion method. RuSA+NP /DC exhibits low overpotentials of 16.6 and 18.8 mV at 10 mA cm-2 in acidic and alkaline electrolytes, respectively. Notably, its mass activities are dramatically improved, which are about 1.1 and 2.4 times those of Pt/C at an overpotential of 50 mV in acidic and alkaline media, respectively. Theoretical calculations reveal that Ru SAs own the most appropriate H* adsorption strength and thus, plays a dominant role for HER in acid electrolyte, while Ru NPs facilitate the dissociation of H2 O that is the rate-determining step in alkaline electrolyte, leading to a remarkable HER activity.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

Guiding the design of oxidation-resistant Fe-based single atom alloy catalysts with insights from configurational space

The high activity and selectivity of Fe-based heterogeneous catalysts toward a variety of reactions that require the breaking of strong bonds are offset in large part by their considerable instability with respect to oxidative deactivation. While it has been shown that the stability of Fe catalysts is considerably enhanced by alloying them with precious metals (even at the single-atom limit), rational design criteria for choosing such secondary metals are still missing. Since oxidative deactivation occurs due to the strong binding of oxygen to Fe and reduction by adsorbed hydrogen mitigates the deactivation, we propose here to use the binding affinity of oxygen and hydrogen adatoms as the basis for rational design. As it would also be beneficial to use cheaper secondary metals, we have scanned over a large subset of 3d-5d mid-to-late transition metal single atoms and computationally determined their effect on the oxygen and hydrogen adlayer binding as a function of chemical potential and adsorbate coverage. We further determine the underlying chemical origins that are responsible for these effects and connect them to experimentally tunable quantities. Our results reveal a reliable periodic trend wherein oxygen binding is weakened greatest as one moves right and down the periodic table. Hydrogen binding shows the same trend only at high (but relevant) coverages and otherwise tends to have its binding slightly increased in all systems. Trends with secondary metal coverage are also uncovered and connected to experimentally tunable parameters.

Theoretical study on water behavior on the copper surfaces

We calculated the adsorption of H, O, OH, and H₂O and the dissociation of H₂O molecule on the Cu(111), Cu(100), and Cu(110) surfaces using density functional theory. H, O, and OH tend to adsorb stably at the highly coordinated dh and h sites on the Cu(111) and Cu(100) surfaces. OH and H tend to adsorb on sb site on the Cu(110) surface. The more charge transfer of the adsorbed substance, the more stable the adsorption. The dissociation product is O+H on the Cu(111) surface, while the dissociation product is OH+H on the Cu(100) and Cu(110) surfaces. Due to the different geometric structures of initial state (IS), transition state (TS), and final state (FS) in the dissociation reaction, the dissociation of water on the copper surface does not establish a linear Brønsted-Evans-Polanyi (BEP) relationship. These results provide theoretical support for the understanding of the interaction between water and metals as well as the behavior of water molecules.

C-H bond activation in light alkanes: a theoretical perspective

Alkanes are the major constituents of natural gas and crude oil, the feedstocks for the chemical industry. The efficient and selective activation of C-H bonds can convert abundant and low-cost hydrocarbon feedstocks into value-added products. Due to the increasing global demand for light alkenes and their corresponding polymers as well as synthesis gas and hydrogen production, C-H bond activation of light alkanes has attracted widespread attention. A theoretical understanding of C-H bond activation in light hydrocarbons via density functional theory (DFT) and microkinetic modeling provides a feasible approach to gain insight into the process and guidelines for designing more efficient catalysts to promote light alkane transformation. This review describes the recent progress in computational catalysis that has addressed the C-H bond activation of light alkanes. We start with direct and oxidative C-H bond activation of methane, with emphasis placed on kinetic and mechanistic insights obtained from DFT assisted microkinetic analysis into steam and dry reforming, and the partial oxidation dependence on metal/oxide surfaces and nanoparticle size. Direct and oxidative activation of the C-H bond of ethane and propane on various metal and oxide surfaces are subsequently reviewed, including the elucidation of active sites, intriguing mechanisms, microkinetic modeling, and electronic features of the ethane and propane conversion processes with a focus on suppressing the side reaction and coke formation. The main target of this review is to give fundamental insight into C-H bond activation of light alkanes, which can provide useful guidance for the optimization of catalysts in future research.

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence

Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.

DFT insights into structural effects of Ni-Cu/CeO2 catalysts for CO selective reaction towards water-gas shift

The water-gas shift (WGS) reaction is a key step in hydrogen production, particularly to meet the high-purity H2 requirement of PEM fuel cells. The catalysts currently employed in large-scale WGS plants require a two-step process to overcome thermodynamic and kinetic limitations. Ni-Cu/CeO2 solids are promising catalysts for the one-step process required for small-scale applications, as the addition of Cu hinders undesired methanation reactions occurring on Ni/CeO2. In this work, we performed calculations on Ni4-xCux/CeO2(111) systems to evaluate the influence of cluster conformation on the selectivity towards water-gas shift. The structure and miscibility of CeO2-supported Ni4-xCux clusters were investigated and compared with those of gas-phase clusters to understand the effect of metal-support interactions. The adsorption of CO onto apical Ni and Cu atoms of Ni4-xCux/CeO2(111) systems was studied, and changes in the C-O bond strength were confirmed at the electronic level by investigating shifts in the 3σ and 1π orbitals. The selectivity towards WGS was evaluated using Brønsted-Evans-Polanyi relations for the C-O activation energy. Overall, a strengthening of the C-O bond and an increase in CO dissociation energy were verified on Cu-containing clusters, explaining the improvement in selectivity of Ni4-xCux/CeO2(111) systems.

CATKINAS: A large-scale catalytic microkinetic analysis software for mechanism auto-analysis and catalyst screening

As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package.

Selective hydrogenation of 5-(hydroxymethyl)furfural to 5-methylfurfural over single atomic metals anchored on Nb2O5

5-Methylfurfural (MF) is a very useful chemical. Selective hydrogenation of biomass platform molecule 5-(hydroxymethyl)furfural (HMF) to MF using H₂ as the reducing agent is very attractive, but challenging because hydrogenation of C=O bond in HMF is more favourable than C–OH both kinetically and thermodynamically, and this route has not been realized. In this work, we prepare isolated single atomic catalysts (SACs) Pt₁/Nb₂O₅-Ov, Pd₁/Nb₂O₅-Ov, and Au₁/Nb₂O₅-Ov, in which single metal atoms are supported on oxygen defective Nb₂O₅ (Nb₂O₅-Ov). It is discovered that the SACs can efficiently catalyze the hydrogenation of HMF to MF using H₂ as the reducing agent with MF selectivity of >99% at complete conversion, while the selectivities of the metal nanocatalysts supported on Nb₂O₅ are very poor. A combination of experimental and density function theory (DFT) studies show that the unique features of the SACs for the reaction result from the cooperation of the Nb and Pt sites near the interface in the Pt₁/Nb₂O₅-Ov. The Pt atoms are responsible for the activation of H₂ and the Nb sites activate C-OH in the reaction. This work opens the way for producing MF by direct hydrogenation of biomass-derived HMF using H₂ as the reductant.

Selective hydrogenation of 5-(hydroxymethyl)furfural (HMF) to 5-Methylfurfural using H2 as reductant is very attractive, but remains challenging. Here, the authors report that isolated single atomic catalysts can catalyze the reaction efficiently with selectivity >99% at complete conversion of HMF.

Theoretical study on water adsorption and dissociation on the nickel surfaces

Using density functional theory methods, H₂O dissociation was investigated on the Ni(111), Ni(100), and Ni(110) surfaces. H and O atom as well as OH species adsorb stably at the high coordination sites. While on the Ni(110) surface, the OH species prefers at the twofold short bridge site because the adsorption on the fourfold hollow site is less feasible due to the increased distances between the nickel atoms. The amount of charge transfer is related to the adsorption stability. The more charge transfer, the more stable the adsorption. The charge transfer decreases in the order of O > OH > H. H₂O molecule adsorbs at the top site in a configuration parallel to the surface. The final products are different for H₂O dissociation due to the different mechanisms. On the Ni(111) surface, the final product is the O atom. On the Ni(100) and Ni(110) surfaces, the most abundant species are OH and H, but the reaction mechanisms were different. It is not necessary to linear BEP relationship for a given reaction on different surfaces. These results could provide fundamental insights into water behaviors and a favorable theoretical basis for further understanding and research on the interaction between water and metal surfaces.

Influence of surface defects on activity and selectivity: a quantitative study of structure sensitivity of Pd catalysts for acetylene hydrogenation

The structure sensitivity of Pd catalysed acetylene hydrogenation is quantitatively examined using a coverage-dependent microkinetic model. Pd(211) was found to be more active than Pd(111), but present a poorer selectivity toward ethylene.

Rational catalyst design for CO oxidation: a gradient-based optimization strategy

In this work, we proposed a gradient-based optimization strategy for rational catalyst design.

Ethanol steam reforming on Rh: microkinetic analyses on the complex reaction network

Reaction networks were generated for ethanol steam reforming to CO and CO2. After the pruning of the networks, the preferred reaction pathways of the CO and CO2 production on Rh(111) were identified and detailed kinetic information was analyzed.

Reaction prediction via atomistic simulation: from quantum mechanics to machine learning

It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN.

Predicting Bond Dissociation Energies and Bond Lengths of Coordinatively Unsaturated Vanadium-Ligand Bonds

Understanding the electronic structure of coordinatively unsaturated transition-metal compounds and predicting their physical properties are of great importance for catalyst design. Bond dissociation energy D_e and bond length r_e are two of the fundamental quantities for which good predictions are important for a successful design strategy. In the present work, recent experimentally measured bond energies and bond lengths of VX diatomic molecules (X = C, N, S) are used as a gauge to consider the utility of a number of electronic structure methods. Single-reference methods are one focus because of their efficiency and utility in practical calculations, and multireference configuration interaction (MRCISD) methods and a composite coupled cluster (CCC) method are a second focus because of their potential high accuracy. The comparison is especially challenging because of the large multireference M diagnostics of these molecules, in the range 0.15-0.19. For the single-reference methods, Kohn-Sham density functional theory (KS-DFT) has been tested with a variety of approximate exchange-correlation functionals. Of these, MOHLYP provides the bond dissociation energies in best agreement with experiments, and BLYP provides the bond lengths that are in best agreement with experiments; but by requiring good performance for both the D_e and r_e of the vanadium compounds, MOHLYP, MN12-L, MGGA_MS1, MGGA_MS0, O3LYP, and M06-L are the most highly recommended functionals. The CCC calculations include up to connected pentuple excitations for the valence electrons and up to connected quadruple excitations for the core-valence terms; this results in highly accurate dissociation energies and good bond lengths. Averaged over the three molecules, the mean unsigned deviation of CCC bond energies from experimental ones is only 0.4 kcal/mol, demonstrating excellent convergence of theory and experiments.