Publications by authors named "Raghavachari K"

Article Synopsis
  • Accurate simulation of electronic excited states in large chromophores is challenging due to the limitations of existing methods, necessitating specialized techniques like Δ-SCF.
  • The study utilizes the initial maximum overlap method (IMOM) to effectively simulate emission energies of numerous dye molecules, achieving high accuracy in predicting emission maxima with a mean absolute error of only 0.27 eV.
  • Spin corrections and linear fit-based adjustments further enhance predictive accuracy, allowing for better classification of dye types and understanding of the method's limitations in complex chemical spaces.
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We present a generalization of the connectivity-based hierarchy (CBH) of isodesmic-based correction schemes to a multilayered fragmentation platform for overall cost reduction while retaining high accuracy. The newly developed multilayered CBH approach, called stepping-stone CBH (SSCBH), is benchmarked on a diverse set of 959 medium-sized organic molecules. Applying SSCBH corrections to the PBEh-D3 density functional resulted in an average error of 0.

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The complexity and size of large molecular systems, such as protein-ligand complexes, pose computational challenges for accurate post-Hartree-Fock calculations. This study delivers a thorough benchmarking of the Molecules-in-Molecules (MIM) method, presenting a clear and accessible strategy for layer/theory selections in post-Hartree-Fock computations on substantial molecular systems, notably protein-ligand complexes. An approach is articulated, enabling augmented computational efficiency by strategically canceling out common subsystem energy terms between complexes and proteins within the supermolecular equation.

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Aromatic foldamers make up a novel class of bioinspired molecules that display helical conformations and have functions that rely on control over their coil-helix folding preferences. While the folding has been extensively examined by experiment, it has rarely been paired with the types of atomic level insights offered by theory. We present the results of all-atom molecular dynamics (MD) simulations to examine the role of solvent polarity on driving the helical folding behavior of the aryl-triazole foldamer.

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We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the ps of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT. The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis).

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Vibrational spectroscopy, including infrared (IR), Raman spectroscopy, and vibrational circular dichroism, is instrumental in determining the structure and composition of molecules. These techniques are highly sensitive to molecular conformations. However, full molecular optimization, necessary for theoretical vibrational spectra, can lead to unintended conformational changes, especially in large biomolecules like polypeptides.

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Fragmentation methods such as MIM (Molecules-in-Molecules) provide a route to accurately model large systems and have been successful in predicting their structures, energies, and spectroscopic properties. However, their use is often limited to systems at equilibrium due to the inherent complications in the choice of fragments in systems away from equilibrium. Furthermore, the presence of charges resulting from any heterolytic bond breaking may increase the fragmentation error.

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Improving the energy efficiency of electrocatalytic reduction of CO requires tuning of redox properties of electrocatalysts to match redox potentials of the substrate. Recently, we introduced nanographenes as ligands for metal complexes for such purposes by taking advantage of size-dependent properties of the conjugated systems. Here, we use computations to investigate the structure dependence of the electrocatalysis at Re(diimine)(CO)Cl complexes with nanographene ligands that contain a polycyclic aromatic hydrocarbon moiety through a pyrazinyl linkage.

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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 method.

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We developed a random forest machine learning (ML) model for the prediction of H and C NMR chemical shifts of nucleic acids. Our ML model is trained entirely on reproducing computed chemical shifts obtained previously on 10 nucleic acids using a Molecules-in-Molecules (MIM) fragment-based density functional theory (DFT) protocol including microsolvation effects. Our ML model includes structural descriptors as well as electronic descriptors from an inexpensive low-level semiempirical calculation (GFN2-xTB) and trained on a relatively small number of DFT chemical shifts (2080 H chemical shifts and 1780 C chemical shifts on the 10 nucleic acids).

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Hybrid methods such as ONIOM (QM:QM) are widely used for the study of local processes in large systems. However, the intrinsic need for system partitioning often leads to a less-than-desirable performance for some important chemical processes. This is due to the missing interactions in the chemically important model region (i.

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Ion mobility spectrometry-mass spectrometry and quantum chemical calculations are used to determine the structures and stabilities of the singly protonated peptide HKPGG. The two peaks making up the IMS distribution are shown to be tautomers differing by the location of the extra proton on either the lysine side chain or the N-terminus. The lysine-protonated tautomer is strongly preferred entropically while being disfavored in terms of the electronic energy and enthalpy.

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The design of advanced optical materials based on triplet states requires knowledge of the triplet energies of the molecular building blocks. To this end, we report the triplet energy of cyanostar () macrocycles, which are the key structure-directing units of small-molecule ionic isolation lattices (SMILES) that have emerged as programmable optical materials. Cyanostar is a cyclic pentamer of covalently linked cyanostilbene units that form π-stacked dimers when binding anions as 2:1 complexes.

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This Perspective reviews connectivity-based hierarchy (CBH), a systematic hierarchy of error-cancellation schemes developed in our group with the goal of achieving chemical accuracy using inexpensive computational techniques ("coupled cluster accuracy with DFT"). The hierarchy is a generalization of Pople's isodesmic bond separation scheme that is based only on the structure and connectivity and is applicable to any organic and biomolecule consisting of covalent bonds. It is formulated as a series of rungs involving increasing levels of error cancellation on progressively larger fragments of the parent molecule.

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Chemists have long benefitted from the ability to understand and interpret the predictions of computational models. With the current shift to more complex deep learning models, in many situations that utility is lost. In this work, we expand on our previously work on computational thermochemistry and propose an interpretable graph network, FragGraph(nodes), that provides decomposed predictions into fragment-wise contributions.

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Photofoldamers are sequence-defined receptors capable of switching guest binding on and off. When two foldamer strands wrap around the guest into 2:1 double helical complexes, cooperativity emerges, and with it comes the possibility to switch cooperativity with light and other stimuli. We use lessons from nonswitchable sequence isomers of aryl-triazole foldamers to guide how to vary the sequence location of azobenzenes from the end () to the interior () and report their impact on the cooperative formation of 2:1 complexes with Cl.

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While accurate wave function theories like CCSD(T) are capable of modeling molecular chemical processes, the associated steep computational scaling renders them intractable for treating large systems or extensive databases. In contrast, density functional theory (DFT) is much more computationally feasible yet often fails to quantitatively describe electronic changes in chemical processes. Herein, we report an efficient delta machine learning (ΔML) model that builds on the Connectivity-Based Hierarchy (CBH) scheme─an error correction approach based on systematic molecular fragmentation protocols─and achieves coupled cluster accuracy on vertical ionization potentials by correcting for deficiencies in DFT.

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We have developed, implemented, and assessed an efficient protocol for the prediction of NMR chemical shifts of large nucleic acids using our molecules-in-molecules (MIM) fragment-based quantum chemical approach. To assess the performance of our approach, MIM-NMR calculations are calibrated on a test set of three nucleic acids, where the structure is derived from solution-phase NMR studies. For DNA systems with multiple conformers, the one-layer MIM method with trimer fragments (MIM1) is benchmarked to get the lowest energy structure, with an average error of only 0.

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Large conjugated carbon framework has been incorporated as the diimine ligand for Re(α-diimine)(CO)Cl complexes with a pyrazinyl linkage, either to increase energy efficiency or to turn them into heterogeneous catalysts for selective electrocatalytic CO reduction. However, there exists a nonmonotonic dependence of CO reduction overpotential on the conjugation size of the ligands. Understanding its origin could facilitate heterogenization of molecular catalysts with improved energy efficiency.

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Hybrid methods such as ONIOM that treat different regions of a large molecule using different methods are widely used to investigate chemical reactions in a variety of materials and biological systems. However, there are inherent sources of significant errors due to the standard treatment of the boundary between the regions using hydrogen link atoms. In particular, an unbalanced charge distribution in the chemically important model region is a potential source of such problems.

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The recognition of boron compounds is well developed as boronic acids but untapped as organotrifluoroborate anions (R-BF ). We are exploring the development of these and other designer anions as anion-recognition motifs by considering them as substituted versions of the parent inorganic ion. To this end, we demonstrate strong and reliable binding of organic trifluoroborates, R-BF , by cyanostar macrocycles that are size-complementary to the inorganic BF progenitors.

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Methods which accurately predict proteinligand binding strengths are critical for drug discovery. In the last two decades, advances in chemical modelling have enabled steadily accelerating progress in the discovery and optimization of structure-based drug design. Most computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding.

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A new strategy for the synthesis of highly versatile cyclobutylboronates via the photosensitized [2+2]-cycloaddition of alkenylboronates and alkenes is presented. The process is mechanistically different from other processes in that energy transfer occurs with the alkenylboronate as opposed to the other alkene. This strategy allows for the synthesis of an array of diverse cyclobutylboronates.

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Quantifying the content of metal-based anticancer drugs within single cancer cells remains a challenge. Here, we used single-cell inductively coupled plasma mass spectrometry to study the uptake and retention of mononuclear (Ir1) and dinuclear (Ir2) Ir photoredox catalysts. This method allowed rapid and precise quantification of the drug in individual cancer cells.

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