Inconsistencies in ab initio evaluations of non-additive contributions of DNA stacking energies

Canonical coupled cluster binding benchmark for nanoscale noncovalent complexes at the hundred-atom scale

In this study, we introduce two datasets for nanoscale noncovalent binding, featuring complexes at the hundred-atom scale, benchmarked using coupled cluster with single, double, and perturbative triple [CCSD(T)] excitations extrapolated to the complete basis set (CBS) limit. The first dataset, L14, comprises 14 complexes with canonical CCSD(T)/CBS benchmarks, extending the applicability of CCSD(T)/CBS binding benchmarks to systems as large as 113 atoms. The second dataset, vL11, consists of 11 even larger complexes, evaluated using the local CCSD(T)/CBS method with stringent thresholds, covering systems up to 174 atoms. We compare binding energies obtained from local CCSD(T) and fixed-node diffusion Monte Carlo (FN-DMC), which have previously shown discrepancies exceeding the chemical accuracy threshold of 1 kcal/mol in large complexes, with the new canonical CCSD(T)/CBS results. While local CCSD(T)/CBS agrees with canonical CCSD(T)/CBS within binding uncertainties, FN-DMC consistently underestimates binding energies in π-π complexes by over 1 kcal/mol. Potential sources of error in canonical CCSD(T)/CBS are discussed, and we argue that the observed discrepancies are unlikely to originate from CCSD(T) itself. Instead, the fixed-node approximation in FN-DMC warrants further investigation to elucidate these binding discrepancies. Using these datasets as reference, we evaluate the performance of various electronic structure methods, semi-empirical approaches, and machine learning potentials for nanoscale complexes. Based on computational accuracy and stability across system sizes, we recommend MP2+aiD(CCD), PBE0+D4, and ωB97X-3c as reliable methods for investigating noncovalent interactions in nanoscale complexes, maintaining their promising performance observed in smaller systems.

Making the most of data: Quantum Monte Carlo postanalysis revisited

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as (functions of) statistical averages of quantities sampled during a calculation. Associated statistical errors of these averages are often estimated. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods (the Straatsma method, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness) for the energy time series given by three QMC methods [diffusion Monte Carlo, full configuration interaction Quantum Monte Carlo (FCIQMC), and coupled cluster Monte Carlo (CCMC)]. From these analyses, we describe a hybrid analysis method which provides reliable error estimates for a series of various lengths of FCIQMC and CCMC's time series. Equally important is the estimation of the appropriate start point of the equilibrated phase. We establish that a simple mean squared error rule method as described by White [K. P. White, Jr., Simulation 69(6), 323 (1997)10.1177/003754979706900601] can provide reasonable estimations.

Ab Initio Evaluation of Complexation Energies for Cyclodextrin-Drug Inclusion Complexes

We investigated the reliability of ab initio methods to predict the binding energies of molecular encapsulation complexes. Vast possibilities for the docking conformations were screened down to a couple of geometries using a semiempirical docking simulation. For the candidates, we applied density functional theory (DFT) with several exchange-correlation (XC) functionals to evaluate the binding energy. We carefully selected and compared the functionals to elucidate the role of the characteristic factors in achieving the XC effects. It is clarified that the improper combination in XC with D3 dispersion force correction leads to overbinding. For achieving a proper combination, the exchange interaction over the longer range to avoid the overbinding was found to be important.