From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

Long-lived organic radicals are promising candidates for the development of high-performance energy solutions such as organic redox batteries, transistors, and light-emitting diodes. However, “stable” organic radicals that remain unreactive for...

The effectiveness of the optical rotation prediction (ORP) basis set for computing specific rotations at the coupled cluster (CC) level has been evaluated for a test set of 14 chiral compounds. For this purpose, the ORP basis set has been developed for the second-row atoms present in the investigated systems (that is, for sulfur, phosphorus, and chlorine). The quality of the resulting set was preliminarily evaluated for seven molecules using time-dependent density-functional theory (TD-DFT). Rotations were calculated with the coupled cluster singles and doubles method (CCSD) as well as the second-order approximate coupled cluster singles and doubles method (CC2) with the correlation-consistent aug-cc-pVDZ and aug-cc-pVTZ basis sets and extrapolated to estimate the complete basis-set (CBS) limit for comparison with the ORP basis set. In the compounds examined here, the ORP calculations on molecules containing only first-row atoms compare favorably with results from the larger aug-cc-pVTZ basis set, in some cases lying closer to the estimated CBS limit, while results for molecules containing second-row atoms indicate that larger correlation-consistent basis sets are necessary to obtain reliable estimates of the CBS limit.

The origin of enantioselectivity in asymmetric catalysis is attributed to the energy difference between lower and higher energy diastereomeric transition states, which are respectively responsible for the formation of major and minor enantiomers. Although the increase in the number of transition state models emphasizes the role of weak non-covalent interactions in asymmetric induction, the strength of such interactions is seldom quantified. Through this article, we propose a simple and effective method of quantifying the total non-covalent interaction in stereocontrolling transition states belonging to a group of three representative asymmetric catalytic reactions involving chiral phosphoric acids. Our method relies on rational partitioning of a given transition state into two (or three) sub-units, such that the complex network of intramolecular interactions can be ameliorated to a set of intermolecular interactions between two sub-units. The computed strength of interaction obtained using the counterpoise (CP) method on suitably partitioned transition states provides improved estimates of non-covalent interactions, which are also devoid of basis set superposition error (BSSE). It has been noted that catalysts decorated with larger aromatic arms provide cumulative non-covalent interactions (C-Hπ, N-Hπ and ππ) to the tune of 10 to 15 kcal mol-1. Fine-tuning of the magnitude and nature of these interactions can provide valuable avenues in the design of asymmetric catalysts.

AQME, Automated Quantum Mechanical Environments, is a free and open-source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use only the desired parts of the program. The code is intended for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi-empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such ANI-1. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble-averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms has been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre-configured workflows are available in various formats, and hands-on video tutorials illustrate their use.