Efficient Calculation of NMR Shielding Constants Using Composite Method Approximations and Locally Dense Basis Sets

Abstract

This paper presents a systematic study of applying composite method approximations with locally dense basis sets (LDBS) to efficiently calculate NMR shielding constants in small and medium-sized molecules. The pcSseg-n series of basis sets are shown to have similar accuracy to the pcS-n series when n ≥ 1 and can slightly reduce computational costs. We identify two different LDBS partition schemes that perform very effectively for density functional calculations. We select a large subset of the recent NS372 database containing 290 H, C, N, and O shielding values evaluated by reference methods on 106 molecules to carefully assess methods of the high, medium, and low computational costs to make practical recommendations. Our assessment covers conventional electronic structure methods (density functional theory and wave function) with global basis calculations, as well as their use in one of the satisfactory LDBS approaches, and a range of composite approaches, also with and without LDBS. Altogether 99 methods are evaluated. On this basis, we recommend different methods to reach three different levels of accuracy and time requirements across the four nuclei considered.

Machine learning-based correction for spin–orbit coupling effects in NMR chemical shift calculations

“…The potential fields of application of the D SO -ML method lie in high-throughput workflows such as structure assignment methods that can be improved when a higher level of theory is used 118 and as an additional ingredient in low-cost composite method approaches that rarely include any relativistic treatment. 119 To conclude, the new D SO -ML method is able to robustly predict SO contributions to NMR chemical shifts for large systems and delivers its full potential when used together with the D corr -ML correction in low-cost NMR prediction schemes.…”

“…3.4.1 Performance for organotin and organolead compounds. In comprehensive benchmark studies, we investigated various DFT methods regarding their ability to predict 119 Sn and 207 Pb NMR chemical shifts. Most of the compounds from these studies were now used to investigate the HALA effect caused by the presence of Sn/Pb atoms and the predictive power of the D SO -ML approach on this quantity.…”

Highly Accurate Prediction of NMR Chemical Shifts from Low-Level Quantum Mechanics Calculations Using Machine Learning

“…Recently, Liang et al presented a systematic investigation on using locally dense basis sets (LDBS) and composite QM methods for chemical shielding calculations, which categorized QM methods into low-level, middle-level, and high-level effectiveness based on a balance of accuracy and computational cost. 21 We selected the ωB97X-V functional 42 in conjunction with the pcSseg-1 basis set 43 as our low-level method. The ωB97X-V functional offers robust and transferable performance for various properties prediction, 44−51 particularly the dipole moment, 46 a simple but effective measure of electron density in polar molecules.…”

“…36 In this work, we present an innovative feature representation obtained from a low-level DFT chemical shielding calculation of the diamagnetic (DIA) and paramagnetic (PARA) shielding tensor elements and combine it with geometric-dependent features that are used as input into a NN model to predict chemical shieldings with training data generated with a composite QM method with nearly equivalent CCSD(T)/ CBS accuracy. 21 In addition, we introduce a novel active learning (AL) training procedure that selects out-of-distribution training data with an increasing number of HAs from a full set of off-equilibrium geometries obtained from the ANI-1 data set. 37 The resulting model achieves a similar level of accuracy as a high-level CCSD(T) calculation with a large basis set but requires computational cost at only a low-level DFT calculation with a tiny basis set.…”

Computation of CCSD(T)-Quality NMR Chemical Shifts via Δ-Machine Learning from DFT

“…One of the most accurate and still feasible theories for calculating chemical shielding constants of fixed, gas-phase molecular structures is the coupled cluster approach, which should include at least singles, doubles, and perturbative triples contributions (CCSD­(T)) . Since the computational cost of such methods is too high for most chemically interesting systems, density functional theory (DFT) has emerged as an efficient compromise , that has the potential to achieve reasonable accuracy for 1 H and 13 C NMR shielding constants, − especially when double hybrid density functionals are applied . However, the accuracy of DFT is often too low for reliable spectra predictions.…”