A first-principles exploration of the conformational space of sodiated di-saccharides assisted by semi-empirical methods and neural network potentials.

Previous exploration of the conformational space of sodiated mono-saccharides using a random search algorithm leads to ∼103 structurally distinct conformers covering an energy range of ∼150 kJ mol-1. Thus, it is reasonable to expect that the number of distinct conformers for a given disaccharide would be on the order of 106. Efficient identification of distinct conformers at the first-principles level has been demonstrated with the assistance of neural network potential (NNP) with an accuracy of ∼1 kJ mol-1 compared to DFT. Leveraging a local minima database of neutral and sodiated glucose (Glc), we develop algorithms to systematically explore the conformation landscape of 19 Glc-based sodiated disaccharides. To accelerate the exploration, the NNP method is implemented. The NNP achieves an accuracy of ∼2.3 kJ mol-1 compared to DFT, offering a comparable quality to that of DFT. Through a multi-model approach integrating DFTB3, NNP and DFT, we can rapidly locate low-energy disaccharide conformers at the first-principles level. The methodology we show here can be used to efficiently explore the potential energy landscape of any di-saccharides when first-principles accuracy is required.

A first-principles exploration of the conformational space of sodiated pyranose assisted by neural network potentials.

Sampling the conformational space of monosaccharides using the first-principles methods is important and as a database of local minima provides a solid base for interpreting experimental measurements such as infrared photo-dissociation (IRPD) spectroscopy or collision-induced dissociation (CID). IRPD emphasizes low-energy conformers and CID can distinguish conformers with distinct reaction pathways. A typical computational approach is to engage empirical or semi-empirical methods to sample the conformational space first, and only selected minima are reoptimized at first-principles levels. In this work, we propose a computational scheme to explore the configurational space of 12 types of sodiated pyranoses with the assistance of a neural network potential (NNP). We demonstrated that it is possible to train an NNP based on the density functional calculations extracted from a previous study on sodiated glucose (Glc), galactose (Gal), and mannose (Man). This NNP yields a better description of the other five types of aldohexoses than the four types of ketohexoses. We further show that such a discrepancy in the accuracy of NNP can be resolved by an active learning scheme where the NNP model is engaged in generating the data and has itself updated. Through this iterative process, we can locate more than 17 000 distinct local minima at the B3LYP/6-311+G(d,p) level and an NNP with an accuracy of 1 kJ mol-1 was created, which can be used for further studies.

A self-adapting first-principles exploration on the dissociation mechanism in sodiated aldohexose pyranoses assisted with neural network potentials.

Understanding the mechanism of collision-induced dissociation (CID) in mono-saccharides with density functional theory (DFT) is challenging because of many possible reaction paths that originate from their high structural diversity. To search for the transition state (TS) from the huge number of conformers, we propose a three-step search scheme with the assistance of neural network potential (NNP). The search starts from a cross-checking of sugars, to a global search of all possible channels, and in the end, an exhaustive exploration around the low-lying channels. The cross-checking step quickly adapts the NNP from the studied molecules to the target ones. The other two steps utilize the adapted NNP to find the available pathways via random sampling of the structures. The study of the CID reactions in all eight types of aldohexose pyranoses was applied using the search scheme. The DFT calculations on AH-0 (Glc, Gal, and Man) in the previous study were utilized to construct an NNP and provide the TS structure database for searching AH-1 (All, Alt, Gul, Ido, and Tal). In total, we identified around 5200 TSs in AH-0 and AH-1, and the final NNP covers an energy range of more than 500 kJ mol-1 with a mean absolute error of energy less than 4 kJ mol-1. The search scheme is useful not only for saccharides but also for highly flexible bio-molecules.

Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning.

Exploring the structure and properties of molecular clusters with accuracy using the ab initio methods is a resource intensive task due to the increasing cost of the ab initio methods and the number of distinct conformers as the size increases. The energy landscape of methanol clusters has been previously explored using computationally efficient empirical models to collect a database of structurally distinct minima, followed by re-optimization using ab initio methods. In this work, we propose a new method that utilizes the database of stable conformers and borrow the fragmentation concept of many-body-expansion (MBE) methods in ab initio methods to train a deep-learning machine learning (ML) model using SchNet. Picking 684 local minima of (CH3OH)5 to (CH3OH)8 from the existing database, we can generate ∼51 000 data points of one-body, two-body, three-body and four-body molecular systems to train an ML model to reach a mean absolute error (MAE) of 3.19 kJ mol-1 (in energy) and 2.48 kJ mol-1 Å-1 (in forces) tested against ab initio calculations up to (CH3OH)14. This ML model is then used to create a database of low energy isomers of (CH3OH)n (n = 15-20). The proposed scheme can be applied to other hydrogen bonded molecular clusters with an accuracy of first-principles methods and computational speed of empirical force-fields.

Enantiodivergent Steglich rearrangement of O-carboxylazlactones catalyzed by a chirality switchable helicene containing a 4-aminopyridine unit.

A pseudo-enantiomeric pair of optically switchable helicenes containing a catalytic 4-N-methylaminopyridine (MAP) bottom unit and a C2-symmetric, (10R,11R)-dimethoxymethyl-dibenzosuberane top template was synthesized. They underwent complementary photoswitching at 290 nm (P/M', <1/>99) and 340 nm (P/M', 91/9) and unidirectional thermo-rotation at 130 °C (P/M', >99/<1). They were utilized to catalyze enantiodivergent Steglich rearrangement of O- to C-carboxylazlactones, with formation of either enantiomer with up to 91% ee (R) and 94% ee (S), respectively.

Stereochemical Course of Wittig Rearrangements of Dihydropyran Allyl Propargyl Ethers.

[2,3]-Wittig rearrangements of sugar-derived dihydropyran allyl propargyl ethers located at the 2- or 4-position have been studied as useful means for extending the carbon chains of the 4- or 2-position with chirality transfer. The stereochemical course of these reactions depends on the following factors: (1) deprotonation of pro-R or pro-S-H, (2) equilibration of the lithiated stereogenic carbanion, (3) conformational inversion during the rearrangement, and (4) concerted [2,3]- or [1,2]-Wittig rearrangement. In some cases, a stepwise mechanism that involves the allyl-C-O bond cleavage is shared as the first step by both the [2,3]- and [1,2]-Wittig rearrangements. The stereochemical courses of the rearrangements are compared among the lithiated reactants to determine the reaction pathways. These mechanisms in the polyoxygenated dihydropyran ring system were further supported by DFT calculations.

A computational study of organic polyradicals stabilized by chromium atoms.

Density functional theory has been used to investigate the properties of organic high spin molecules. The M05/cc-pVDZ calculations predict a septet ground state for the 2,3,6,7,10,11-hexahydro-1,4,5,8,9,12-hexaoxocoronene-2,3,6,7,10,11-hexayl radical (coronene-6O). The computations show further that the formation of intermolecular carbon-carbon bonds yields a singlet ground state for the dimer rather than a possible tridectet state as expected from the monomer's multiplicity. A benzene molecule placed between coronene-6O molecules leads to the desired high-spin cluster, but the overall stability of the cluster is low. A chromium atom inserted between two peripheral C(6) rings of coronene-6O yields a sandwich structure with the expected tridectet ground state and a binding energy which is 15 times larger than the corresponding tridectet dimer stabilized by a benzene molecule. The presented DFT calculations suggest that a chromium atom can effectively link organic polyradicals to larger magnetic units.