Constructing Mixed Density Functionals for Describing Dissociative Chemisorption on Metal Surfaces: Basic Principles

The production of a majority of chemicals involves heterogeneous catalysis at some stage, and the rates of many heterogeneously catalyzed processes are governed by transition states for dissociative chemisorption on metals. Accurate values of barrier heights for dissociative chemisorption on metals are therefore important to benchmarking electronic structure theory in general and density functionals in particular. Such accurate barriers can be obtained using the semiempirical specific reaction parameter (SRP) approach to density functional theory. However, this approach has thus far been rather ad hoc in its choice of the generic expression of the SRP functional to be used, and there is a need for better heuristic approaches to determining the mixing parameters contained in such expressions. Here we address these two issues. We investigate the ability of several mixed, parametrized density functional expressions combining exchange at the generalized gradient approximation (GGA) level with either GGA or nonlocal correlation to reproduce barrier heights for dissociative chemisorption on metal surfaces. For this, seven expressions of such mixed density functionals are tested on a database consisting of results for 16 systems taken from a recently published slightly larger database called SBH17. Three expressions are derived that exhibit high tunability and use correlation functionals that are either of the PBE GGA form or of one of two limiting nonlocal forms also describing the attractive van der Waals interaction in an approximate way. We also find that, for mixed density functionals incorporating GGA correlation, the optimum fraction of repulsive RPBE GGA exchange obtained with a specific GGA density functional is correlated with the charge-transfer parameter, which is equal to the difference in the work function of the metal surface and the electron affinity of the molecule. However, the correlation is generally not large and not large enough to obtain accurate guesses of the mixing parameter for the systems considered, suggesting that it does not give rise to a very effective search strategy.

Highly accurate σ- and τ-functionals for beyond-RPA methods with approximate exchange kernels

σ-Functionals are promising new developments for the Kohn-Sham correlation energy based upon the direct Random Phase Approximation (dRPA) within the adiabatic connection formalism, providing impressive improvements over dRPA for a broad range of benchmarks. However, σ-functionals exhibit a high amount of self-interaction inherited from the approximations made within dRPA. Inclusion of an exchange kernel in deriving the coupling-strength-dependent density-density response function leads to so-called τ-functionals, which - apart from a fourth-order Taylor series expansion - have only been realized in an approximate fashion so far to the best of our knowledge, most notably in the form of scaled σ-functionals. In this work, we derive, optimize, and benchmark three types of σ- and τ-functionals including approximate exchange effects in the form of an antisymmetrized Hartree kernel. These functionals, based on a second-order screened exchange type contribution in the adiabatic connection formalism, the electron-hole time-dependent Hartree-Fock kernel (eh-TDHF) otherwise known as RPA with exchange (RPAx), and an approximation thereof known as approximate exchange kernel (AXK), are optimized on the ASCDB database using two new parametrizations named A1 and A2. In addition, we report a first full evaluation of σ- and τ-functionals on the GMTKN55 database, revealing our exchange-including functionals to considerably outperform existing σ-functionals while being highly competitive with some of the best double-hybrid functionals of the original GMTKN55 publication. In particular, the σ-functionals based on AXK and τ-functionals based on RPAx with PBE0 reference stand out as highly accurate approaches for a wide variety of chemically relevant problems.

Current stage and future development of Belgrade collisional and radiative databases/datasets of importance for molecular dynamics

Atomic and molecular (A&M) databases that contain information about species, their identities and radiative/collisional processes are essential and helpful tools that are utilized in many fields of physics, chemistry, and chem/phys-informatics. Errors or inconsistencies in the datasets are a serious issue since they can lead to inaccurate predictions and generate problems with the modeling. This demonstrates that data curation efforts around A&M databases are still indispensable and that in the curation process studious attention is required. Therefore, we herein present research activities around Belgrade "nodes" - datasets of collision/radiative cross-sections and rates needed for spectroscopy analysis in various A&M, optical and plasma physics fields. Methodologies of our research and both present and future aspects of the applications are explained. We explored the possibility to extend our nodes towards building a new database on Judd-Ofelt parameters by using machine learning in order to predict optical properties of luminescence materials. In addition, we hope that public availability of our datasets and their graphical representations will also motivate others to investigate the potential of these data.

Comparative Study of Vibrational Raman Optical Activity with Different Time-Dependent Density Functional Approximations: The VROA36 Database

A new database, VROA36, is introduced to investigate the performance of computational approaches for vibrational Raman optical activity (VROA) calculations. The database is composed of 36 molecules with known experimental VROA spectra. It includes 93 conformers. Normal modes calculated with B3LYP-D3(BJ)/def2-TZVP are used to compute the VROA spectra with four functionals, B3LYP-D3(BJ), ωB97X-D, M11, and optimally tuned LC-PBE, as well as several basis sets. SimROA indices and frequency scaling factors are used to compare calculated spectra with each other and with experimental data. The four functionals perform equally well independently of the basis set and usually achieve good agreement with the experimental data. For molecules in near- or at-resonance conditions, the inclusion of a complex (damped) linear response approach is important to obtain physically meaningful VROA intensities. The use of any of the tested functional approximations with the def2-SVPD Gaussian-type basis set, or a basis of similar flexibility, can be recommended for efficient and reliable theoretical VROA studies.

CIDER: An Expressive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints

Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, including accuracy, numerical stability, and transferability across chemical space. In this work, we introduce a set of nonlocal features of the density called the CIDER formalism, which we use to train a Gaussian process model for the exchange energy that obeys the critical uniform scaling rule for exchange. The resulting CIDER exchange functional is significantly more accurate than any semilocal functional tested here, and it has good transferability across main-group molecules. This work therefore serves as an initial step toward more accurate exchange functionals, and it also introduces useful techniques for developing robust, physics-informed XC models via ML.

Extrapolating DFT Toward the Complete Basis Set Limit: Lessons from the PBE Family of Functionals

Extrapolation of density functional theory results from 2- and 3-ζ calculations is a promising method for extracting higher accuracy data from calculations of systems at the affordability limit. In this work, the author presents formulas for the determination of extrapolation parameters, which account for the makeup of the density functional approximation. The formulas are fitted to reproduce the complete basis set limit energies of PBE and related density functional approximations, using a set of 30 singlet diatomics. Their performance is extensively evaluated using standard benchmark data sets. The current systematically derived expressions are shown to be transferrable outside the PBE family of functional approximations, with the resulting extrapolation parameters outperforming the previous, less-systematic values. A good performance of [2,3]-ζ extrapolations for interaction energies of systems with significant noncovalent character is confirmed and holds even in systems of ∼100 atoms in size.

Density functionals with full nonlocal exchange, nonlocal rung-3.5 correlation, and D3 dispersion: Combined accuracy for general main-group thermochemistry, kinetics, and noncovalent interactions

We introduce the HF-R35-D3(BJ) functional combining full nonlocal exact (Hartree-Fock-like, HF) exchange, inexpensive rung-3.5 correlation constructed from nonlocal one-electron operators, and nonlocal D3 dispersion corrections. HF-R35-D3(BJ) is among the first full-exact-exchange functionals offering competitive accuracy for general main-group thermochemistry, kinetics, and noncovalent interactions. HF-R35-D3(BJ) gives weighted mean absolute deviation WTMAD-2 8.5 kcal/mol across the entire GMTKN55 dataset, outperforming most dispersion-corrected semilocal functionals and approaching the accuracy of dispersion-corrected global hybrids. This requires six fitted parameters, three each in the nonlocal correlation and dispersion corrections. Full nonlocal exchange appears to help give accurate binding energies and reasonable energy orderings for water hexamers. These results motivate continued exploration of inexpensive nonlocal correlation corrections to nonlocal exchange.

Accelerating seminumerical Fock-exchange calculations using mixed single- and double-precision arithmethic

We investigate the applicability of single-precision (fp32) floating point operations within our linear-scaling, seminumerical exchange method sn-LinK [Laqua et al., J. Chem. Theory Comput. 16, 1456 (2020)] and find that the vast majority of the three-center-one-electron (3c1e) integrals can be computed with reduced numerical precision with virtually no loss in overall accuracy. This leads to a near doubling in performance on central processing units (CPUs) compared to pure fp64 evaluation. Since the cost of evaluating the 3c1e integrals is less significant on graphic processing units (GPUs) compared to CPU, the performance gains from accelerating 3c1e integrals alone is less impressive on GPUs. Therefore, we also investigate the possibility of employing only fp32 operations to evaluate the exchange matrix within the self-consistent-field (SCF) followed by an accurate one-shot evaluation of the exchange energy using mixed fp32/fp64 precision. This still provides very accurate (1.8 µEh maximal error) results while providing a sevenfold speedup on a typical "gaming" GPU (GTX 1080Ti). We also propose the use of incremental exchange-builds to further reduce these errors. The proposed SCF scheme (i-sn-LinK) requires only one mixed-precision exchange matrix calculation, while all other exchange-matrix builds are performed with only fp32 operations. Compared to pure fp64 evaluation, this leads to 4-7× speedups for the whole SCF procedure without any significant deterioration of the results or the convergence behavior.

Replacing hybrid density functional theory: motivation and recent advances

Density functional theory (DFT) is the most widely-used electronic structure approximation across chemistry, physics, and materials science. Every year, thousands of papers report hybrid DFT simulations of chemical structures, mechanisms, and spectra. Unfortunately, hybrid DFT's accuracy is ultimately limited by tradeoffs between over-delocalization and under-binding. This review summarizes these tradeoffs, and introduces six modern attempts to go beyond them while maintaining hybrid DFT's relatively low computational cost: DFT+U, self-interaction corrections, localized orbital scaling corrections, local hybrid functionals, real-space nondynamical correlation, and our rung-3.5 approach. The review concludes with practical suggestions for DFT users to identify and mitigate these tradeoffs' impact on their simulations.

Competition between cyclization and unusual Norrish type I and type II nitro-acyl migration pathways in the photouncaging of 1-acyl-7-nitroindoline revealed by computations

The 7-nitroindolinyl family of caging chromophores has received much attention in the past two decades. However, its uncaging mechanism is still not clearly understood. In this study, we performed state-of-the-art density functional theory calculations to unravel the photo-uncaging mechanism in its entirety, and we compared the probabilities of all plausible pathways. We found competition between a classical cyclization and an acyl migration pathway, and here we explain the electronic and steric reasons behind such competition. The migration mechanism possesses the characteristics of a combined Norrish type I and a 1,6-nitro-acyl variation of a Norrish type II mechanism, which is reported here for the first time. We also found negligible energetic differences in the uncaging mechanisms of the 4-methoxy-5,7-dinitroindolinyl (MDNI) cages and their mononitro analogues (MNI). We traced the experimentally observed improved quantum yields of MDNI to a higher population of the reactants in the triplet surface. This fact is supported by a more favorable intersystem crossing due to the availability of a higher number of triplet excited states with the correct symmetry in MDNI than in MNI. Our findings may pave the way for improved cage designs that possess higher quantum yields and a more efficient agonist release.

Competition between cyclization and unusual Norrish type I and type II nitro-acyl migration pathways in the photouncaging of 1-acyl-7-nitroindoline revealed by computations

The 7-nitroindolinyl family of caging chromophores has received much attention in the past two decades. However, its uncaging mechanism is still not clearly understood. In this study, we performed state-of-the-art density functional theory calculations to unravel the photo-uncaging mechanism in its entirety, and we compared the probabilities of all plausible pathways. We found competition between a classical cyclization and an acyl migration pathway, and here we explain the electronic and steric reasons behind such competition. The migration mechanism possesses the characteristics of a combined Norrish type I and a 1,6-nitro-acyl variation of a Norrish type II mechanism, which is reported here for the first time. We also found negligible energetic differences in the uncaging mechanisms of the 4-methoxy-5,7-dinitroindolinyl (MDNI) cages and their mononitro analogues (MNI). We traced the experimentally observed improved quantum yields of MDNI to a higher population of the reactants in the triplet surface. This fact is supported by a more favorable intersystem crossing due to the availability of a higher number of triplet excited states with the correct symmetry in MDNI than in MNI. Our findings may pave the way for improved cage designs that possess higher quantum yields and a more efficient agonist release.

Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and ranking probability matrix. I. Theory

The comparison of benchmark error sets is an essential tool for the evaluation of theories in computational chemistry. The standard ranking of methods by their mean unsigned error is unsatisfactory for several reasons linked to the non-normality of the error distributions and the presence of underlying trends. Complementary statistics have recently been proposed to palliate such deficiencies, such as quantiles of the absolute error distribution or the mean prediction uncertainty. We introduce here a new score, the systematic improvement probability, based on the direct system-wise comparison of absolute errors. Independent of the chosen scoring rule, the uncertainty of the statistics due to the incompleteness of the benchmark datasets is also generally overlooked. However, this uncertainty is essential to appreciate the robustness of rankings. In the present article, we develop two indicators based on robust statistics to address this problem: P_inv, the inversion probability between two values of a statistic, and P_r, the ranking probability matrix. We demonstrate also the essential contribution of the correlations between error sets in these scores comparisons.