Fragment-Based Approaches for Supramolecular Interaction Energies: Applications to Foldamers and Their Complexes with Anions

Reformulation of All ONIOM-Type Molecular Fragmentation Approaches and Many-Body Theories Using Graph-Theory-Based Projection Operators: Applications to Dynamics, Molecular Potential Surfaces, Machine Learning, and Quantum Computing

We present a graph-theory-based reformulation of all ONIOM-based molecular fragmentation methods. We discuss applications to (a) accurate post-Hartree-Fock AIMD that can be conducted at DFT cost for medium-sized systems, (b) hybrid DFT condensed-phase studies at the cost of pure density functionals, (c) reduced cost on-the-fly large basis gas-phase AIMD and condensed-phase studies, (d) post-Hartree-Fock-level potential surfaces at DFT cost to obtain quantum nuclear effects, and (e) novel transfer machine learning protocols derived from these measures. Additionally, in previous work, the unifying strategy discussed here has been used to construct new quantum computing algorithms. Thus, we conclude that this reformulation is robust and accurate.

Investigating the Stacking Interactions Responsible for Driving 3D Self-Association of Tricarb Macrocycles

We have investigated the noncovalent forces that play a crucial role in the three-dimensional (3D) self-association of the tricarb macrocycle (composed of alternating triazoles and carbazoles) to understand the multilayer stacks observed through electron microscopy. To explore this idea quantitatively, we have investigated a stacked dimer model of tricarb, where we consider homochiral as well as heterochiral forms of the dimer. We have computed the rotational potential energy surface of the dimer by conducting an angle-dependent scan between the two macrocycles using different levels of theory including the RI-MP2 ab initio method. We observe that dimers oriented at an angle of 60° exhibit the highest stability, while a secondary minimum is observed at an angle of 30°. While density functional theory (DFT) describes the behavior of both minima very close to that obtained with RI-MP2, semiempirical and MM models appear to obtain only a shoulder instead of the second minimum. To further understand the underlying interactions responsible for stabilizing the self-assembly of the macrocycles, we employed energy decomposition analysis (EDA) using SAPT0. This quantitative assessment allowed us to identify the major contributing noncovalent interactions, including electrostatic, exchange-repulsion, dispersion, and induction. Finally, we expanded our study to evaluate the accuracy of the MIM (molecules-in-molecules) fragmentation methodology in capturing the crucial π-stacking interactions. Our benchmarking results using the MIM method accurately replicated the angle-dependent PES results. This shows the efficacy of MIM in predicting the noncovalent interactions responsible for the construction of 3D and other higher-order nanoarchitectures for tricarb and related compounds.

Insight into pyrolysis mechanism of 1,2-propylene glycol: Based on density functional theory and wavefunction analysis

The multiple thermal decomposition mechanisms of 1,2-propylene glycol are studied through theoretical calculation and experiment, including carbon chain break, dehydrogenation and dehydration mechanism. The wavefunction is employed to analyze the decomposition process from a micro perspective. DLPNO-CCSD(T)/CBS method is engaged in establishing potential energy surface. The results reveal that the dehydration and carbon chain break mechanism are the primary pyrolysis paths, and the former is the dominant pyrolysis mechanism at low temperature, while the latter is applicable at the high temperature. The pyrolysis products are mainly acetaldehyde, propanal and acetone, which is consistent with experimental results. Besides, the comparison results of 1,2-propylene glycol and glycerol pyrolysis products indicate that the increment of hydroxyls are conducive to the generation of carbonyl compounds during the polyol thermal decomposition. This work is aimed to comprehensively investigate the pyrolysis mechanism of 1,2-propylene glycol and provide the valuable thermodynamics and kinetic data for developing efficient polyol pyrolysis technology. Furthermore, it provides a reference for choosing low-toxic tobacco humectant.

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

Electrostatically embedded molecules-in-molecules approach and its application to molecular clusters

We report the application of our fragment-based quantum chemistry model MIM (Molecules-In-Molecules) with electrostatic embedding. The method is termed "EE-MIM (Electrostatically Embedded Molecules-In-Molecules)" and accounts for the missing electrostatic interactions in the subsystems resulting from fragmentation. Point charges placed at the atomic positions are used to represent the interaction of each subsystem with the rest of the molecule with minimal increase in the computational cost. We have carefully calibrated this model on a range of different sizes of clusters containing up to 57 water molecules. The fragmentation methods have been applied with the goal of reproducing the unfragmented total energy at the MP2/6-311G(d,p) level. Comparative analysis has been carried out between MIM and EE-MIM to gauge the impact of electrostatic embedding. Performance of several different parameters such as the type of charge and levels of fragmentation are analyzed for the prediction of absolute energies. The use of background charges in subsystem calculations improves the performance of both one- and two-layer MIM while it is noticeably important in the case of one-layer MIM. Embedded charges for two-layer MIM are obtained from a full system calculation at the low-level. For one-layer MIM, in the absence of a full system calculation, two different types of embedded charges, namely, Geometry dependent (GD) and geometry independent (GI) charges, are used. A self-consistent procedure is employed to obtain GD charges. We have further tested our method on challenging charged systems with stronger intermolecular interactions, namely, protonated ammonia clusters (containing up to 30 ammonia molecules). The observations are similar to water clusters with improved performance using embedded charges. Overall, the performance of NPA charges as embedded charges is found to be the best.

Fragment-based quantum mechanical approach to biomolecules, molecular clusters, molecular crystals and liquids

To study large molecular systems beyond the system size that the current state-of-the-art ab initio electronic structure methods could handle, fragment-based quantum mechanical (QM) approaches have been developed over the past years, and proved to be efficient in dealing with large molecular systems at various ab initio levels. According to the fragmentation approach, a large molecular system can be divided into subsystems (fragments), and subsequently the property of the whole system can be approximately obtained by taking a proper combination of the corresponding terms of individual fragments. Therefore, the standard QM calculation of a large system could be circumvented by carrying out a series of calculations on small fragments, which significantly promotes computational efficiency. The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method is one of the fragment-based QM approaches which has been developed by our research group in recent years. This Perspective presents the theoretical framework of this fragmentation method and its applications in biomolecules, molecular clusters, molecular crystals and liquids, including total energy calculation, protein-ligand/protein binding affinity prediction, geometry optimization, vibrational spectrum simulation, ab initio molecular dynamics simulation, and prediction of excited-state properties.

Energy-Screened Many-Body Expansion: A Practical Yet Accurate Fragmentation Method for Quantum Chemistry

We introduce an implementation of the truncated many-body expansion, MBE(n), in which the n-body corrections are screened using the effective fragment potential force field, and only those that exceed a specified energy threshold are computed at a quantum-mechanical level of theory. This energy-screened MBE(n) approach is tested at the n = 3 level for a sequence of water clusters, (H₂O)_N=6-34. A threshold of 0.25 kJ/mol eliminates more than 80% of the subsystem electronic structure calculations and is even more efficacious in that respect than is distance-based screening. Even so, the energy-screened MBE(3) method is faithful to a full-system quantum chemistry calculation to within 1-2 kJ/mol/monomer, even in good quality basis sets such as aug-cc-pVTZ. These errors can be reduced by means of a two-layer approach that involves a Hartree-Fock calculation for the entire cluster. Such a correction proves to be necessary in order to obtain accurate relative energies for conformational isomers of (H₂O)₂₀, but the cost of a full-system Hartree-Fock calculation remains smaller than the cost of three-body subsystem calculations at correlated levels of theory. At the level of second-order Møller-Plesset perturbation theory (MP2), a screened MBE(3) calculation plus a full-system Hartree-Fock calculation is less expensive than a full-system MP2 calculation starting at N = 12 water molecules. This is true even if all MBE(3) subsystem calculations are performed on a single 40-core compute node, i.e., without significant parallelization. Energy-screened MBE(n) thus provides a fragment-based method that is accurate, stable in large basis sets, and low in cost, even when the latter is measured in aggregate computer time.

Fantasy versus reality in fragment-based quantum chemistry

Since the introduction of the fragment molecular orbital method 20 years ago, fragment-based approaches have occupied a small but growing niche in quantum chemistry. These methods decompose a large molecular system into subsystems small enough to be amenable to electronic structure calculations, following which the subsystem information is reassembled in order to approximate an otherwise intractable supersystem calculation. Fragmentation sidesteps the steep rise (with respect to system size) in the cost of ab initio calculations, replacing it with a distributed cost across numerous computer processors. Such methods are attractive, in part, because they are easily parallelizable and therefore readily amenable to exascale computing. As such, there has been hope that distributed computing might offer the proverbial "free lunch" in quantum chemistry, with the entrée being high-level calculations on very large systems. While fragment-based quantum chemistry can count many success stories, there also exists a seedy underbelly of rarely acknowledged problems. As these methods begin to mature, it is time to have a serious conversation about what they can and cannot be expected to accomplish in the near future. Both successes and challenges are highlighted in this Perspective.

Solvent Screening in Zwitterions Analyzed with the Fragment Molecular Orbital Method

Based on induced solvent charges, a new model of solvent screening is developed in the framework of the fragment molecular orbital combined with the polarizable continuum model. The developed model is applied to analyze interactions in a prototypical zwitterionic system, sodium chloride in water, and it is shown that the large underestimation of the interaction in the original solvent screening based on local charges is successfully corrected. The model is also applied to a complex of the Trp-cage (PDB: 1L2Y ) miniprotein with an anionic ligand, and the physical factors determined protein-ligand binding in solution are unraveled.

Exploring Reaction Energy Profiles Using the Molecules-in-Molecules Fragmentation-Based Approach

The Molecules-in-Molecules (MIM) fragmentation-based approach has been successfully used in previous studies to obtain the energies, optimized geometries, and spectroscopic properties of large molecular systems. The present work delineates a protocol to study the potential energy profiles for multistep chemical reactions using the MIM methodology. In a complex multistep chemical reaction, the fragmentation scheme needs to be changed as the reacting species transition into a new reaction step, resulting in a discontinuity in the potential energy curve of the reaction. In our approach, the fragmentation scheme for a particular step in a reaction is chosen on the basis of the nature of the bonding changes associated with that step. Thus, the reactant, transition state, and product are treated consistently throughout the reaction step, leading to an accurate energy barrier for that step. The discontinuity now occurs in describing the energies of reaction intermediates at the transition point between two reaction steps that are treated by two different fragmentation schemes. To address this issue, we propose a systematic procedure for obtaining continuous potential energy curves that are least shifted from their initial positions. The corrected MIM potential energy curves are continuous with activation energies preserved. Following this approach, energy profiles of complex reactions involving large molecular species can be obtained at high levels of theory with a reasonable computational cost.