arXiv subjects

Raghunathan Ramakrishnan

Publications and source records attributed to Raghunathan Ramakrishnan.

At least 19 recordsLinked to original sources

arXiv2026

Assessing excited-state geometry optimization strategies for adiabatic photophysical energies

Accurate prediction of adiabatic $0$-$0$ excited-state energies is crucial for modeling molecular photophysical processes. Here, we benchmark computational strategies for evaluating excited-state energies and singlet-triplet gaps obtained using different geometry-optimization strategies, including time-dependent density functional theory (TDDFT), spin-unrestricted Kohn-Sham (UKS) DFT for triplet states (${\rm T}_1$), and state-specific orbital-optimized UKS (ssUKS) DFT for singlet excited states (${\rm S}_1$). Zero-point vibrational energy corrections are evaluated consistently at the optimized geometries and combined with ADC(2) excitation energies for comparison with experimental anion photoelectron spectroscopy data for a representative set of molecules. Among the protocols considered, adiabatic $0$-$0$ energies evaluated at TDDFT-optimized ${\rm S}_1$ and ${\rm T}_1$ geometries show the best agreement with experiment, with a mean absolute error below 0.1 eV. Replacing these geometries with UKS-optimized ${\rm T}_1$ and ssUKS-optimized ${\rm S}_1$ structures yields comparable accuracy. Vertical excitation energies are substantially more sensitive to the choice of geometry than the corresponding ${\rm S}_1$-${\rm T}_1$ gaps, which are comparatively more robust because of partial error cancellation. As a larger case study, we examine rubrene and find that UKS/ssUKS-based geometries remain useful for evaluating singlet-fission energetics. Overall, UKS/ssUKS-based workflows provide an efficient and accurate route to excited-state geometry optimization and to the evaluation of adiabatic $0$-$0$ energies for states with dominant single-determinant character.

physics.chem-ph↗

arXiv2026

A Chemical Space Perspective on Diastereomeric Barriers in Alkylperoxy-to-Hydroperoxyalkyl Isomerization

Low-temperature hydrocarbon autooxidation involves radical intermediates whose reactivity depends not only on the stereochemistry of the intermediates themselves, but also on that of the transient species encountered along the reaction path. This study offers large-scale evidence for the importance of stereochemistry in low-temperature autooxidation by propagating stereochemical information from 498 C1-C7 hydrocarbons through radical formation, $\mathrm{O_2}$ addition, and the isomerization of alkylperoxy ROO radicals to hydroperoxyalkyl QOOH radicals. The resulting dataset comprises density-functional-theory-level data for 5,356 species, including 2,324 cyclic diastereomeric transition states associated with 1,162 unique ROO -> QOOH isomerization reactions, with transition-state connectivity confirmed by intrinsic reaction coordinate analysis. Explicit stereochemical treatment reveals that diastereomeric pathways may be either degenerate or separated by more than 60 kcal/mol, with the magnitude of these differences governed by steric strain at the carbon bearing the peroxyl group. These results show that constitutionally collapsed molecular representations can systematically miss kinetically relevant reactive channels and provide a foundation for stereochemistry-aware mechanism generation, rate estimation, and predictive combustion modeling.

physics.chem-ph↗

arXiv2025

Insights into Symmetry and Substitution Patterns Governing Singlet-Triplet Energy Gap in the Chemical Space of Azaphenalenes

Molecules that violate Hund's rule by exhibiting an inverted singlet-triplet gap (STG), where the first excited singlet (S$_1$) lies below the triplet (T$_1$), are rare but hold great promise as efficient fifth-generation light emitters. Azaphenalenes (APs) represent one of the few known molecular classes capable of such inversion of the S$_1$/T$_1$ energy ordering, yet a systematic exploration of all unique APs is lacking. Here, we investigate 104 distinct APs and classify them based on their adherence to or deviation from Hund's rule using S$_1$-T$_1$ gaps computed with the second-order coupled-cluster method employing the Laplace transform (L-CC2). To capture substitution-dependent pseudo-Jahn-Teller distortions that are inadequately described by MP2 and DFT methods, we employ focal-point extrapolation scheme to obtain near-CCSD(T)/cc-pVTZ-quality geometries. We find three APs to undergo $D_{\rm 3h} \rightarrow C_{\rm 3h}$ and ten to show $C_{\rm 2v}\rightarrow C_{\rm s}$ symmetry lowering, leading to a total of 117 configurations of 104 unique APs. Our study identifies top candidates with inverted STGs, revealing how substitution and symmetry-lowering modulate these gaps to uncover new stable AP cores that provide promising targets for designing molecular light-emitters.

physics.chem-ph↗

arXiv2025

Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations

Accurate prediction of nuclear magnetic resonance (NMR) shielding with machine learning (ML) models remains a central challenge for data-driven spectroscopy. We present atomic variants of the Coulomb matrix (aCM) and bag-of-bonds (aBoB) descriptors, and extend them using radial basis functions (RBFs) to yield smooth, per-atom representations (aCM-RBF, aBoB-RBF). Local structural information is incorporated by augmenting each atomic descriptor with contributions from the n nearest neighbors, resulting in the family of descriptors, aCM-RBF(n) and aBoB-RBF(n). For 13C shielding prediction on the QM9NMR dataset (831,925 shielding values across 130,831 molecules), aBoB-RBF(4) achieves an out-of-sample mean error of 1.69 ppm, outperforming models reported in previous studies. While explicit three-body descriptors further reduce errors at a higher cost, aBoB-RBF(4) offers the best balance of accuracy and efficiency. Benchmarking on external datasets comprising larger molecules (GDBm, Drug12/Drug40, and pyrimidinone derivatives) confirms the robustness and transferability of aBoB-RBF(4), establishing it as a practical tool for ML-based NMR shielding prediction.

physics.chem-ph↗

arXiv2025

Machine-Learned Potentials for Solvation Modeling

Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computational cost. MLPs approximate the underlying potential energy surface, enabling efficient computation of energies and forces in solvated systems, and are capable of accounting for effects such as hydrogen bonding, long-range polarization, and conformational changes. This review surveys the development and application of MLPs in solvation modeling. We summarize the theoretical basis of MLP-based energy and force predictions and present a classification of MLPs based on training targets, model types, and design choices related to architectures, descriptors, and training protocols. Integration into established solvation workflows is discussed, with case studies spanning small molecules, interfaces, and reactive systems. We conclude by outlining open challenges and future directions toward transferable, robust, and physically grounded MLPs for solvation-aware atomistic modeling.

physics.chem-ph↗

arXiv2025

Leveraging the Bias-Variance Tradeoff in Quantum Chemistry for Accurate Negative Singlet-Triplet Gap Predictions: A Case for Double-Hybrid DFT

Molecules that have been suggested to violate the Hund's rule, having a first excited singlet state (S$_1$) energetically below the triplet state (T$_1$), are rare. Yet, they hold the promise to be efficient light emitters. Their high-throughput identification demands exceptionally accurate excited-state modeling to minimize qualitatively wrong predictions. We benchmark twelve S$_1$-T$_1$ energy gaps to find that the local-correlated versions of ADC(2) and CC2 excited state methods deliver excellent accuracy and speed for screening medium-sized molecules. Notably, we find that double-hybrid DFT approximations (e.g., B2GP-PLYP and PBE-QIDH) exhibit high mean absolute errors ($>100$ meV) despite very low standard deviations ($\approx10$ meV). Exploring their parameter space reveals that a configuration with 75% exchange and 55% correlation, which reduces the mean absolute error to below 5 meV, but with an increased variance. Using this low-bias parameterization as an internal reference, we correct the systematic error while maintaining low variance, effectively combining the strengths of both low-bias and low-variance DFT parameterizations to enhance overall accuracy. Our findings suggest that low-variance DFT methods, often overlooked due to their high bias, can serve as reliable tools for predictive modeling in first-principles molecular design. The bias-correction data-fitting procedure can be applied to any general problem where two flavors of a method, one with low bias and another with low variance, have been identified a priori.

physics.chem-ph↗

arXiv2025

Unlocking Inverted Singlet-Triplet Gap in Alternant Hydrocarbons with Heteroatoms

Fifth-generation organic light-emitting diodes exhibit delayed fluorescence even at low temperatures, enabled by exothermic reverse intersystem crossing from a negative singlet-triplet gap (STG), where the first excited singlet lies anomalously below the triplet. This phenomenon -- termed delayed fluorescence from inverted singlet and triplet states (DFIST) -- has been experimentally confirmed only in two triangular molecules with a 12-annulene periphery and a central nitrogen atom. Here, we report a high-throughput virtual screening of 30,797 BN-substituted polycyclic aromatic hydrocarbons derived from 77 parent scaffolds (2--6 rings). Using a multi-level workflow combining structural stability criteria with accurate L-CC2 excited-state calculations, we identify 72 heteroaromatic candidates with STGs$<0$. Notably, this includes BN-helicenes, where inversion arises from through-space charge-transfer states. Several systems exhibit non-zero oscillator strengths, supporting their potential as fluorescent emitters. Our findings reveal new design motifs for DFIST beyond known frameworks, expanding the chemical space for next-generation emitters based on heteroatom-embedded aromatic systems.

physics.chem-ph↗

arXiv2024

Probabilistic Parallels in the Classical Limit of Quantum Mechanical Models

At large quantum numbers, the probability densities for particle-in-a-box or simple harmonic oscillator converge to the classical result upon coarse-graining the quantum mechanical probability densities by introducing a finite resolution in the measurement of the particle's position. This resolution in the position can be related to the resolution of the secondary total angular momentum quantum number ($m$) when interpreting the probabilistic outcomes of the Stern--Gerlach-type thought experiments for large values of the angular momentum quantum numbers ($j$).

quant-ph↗

arXiv2024

Influence of Pseudo-Jahn-Teller Activity on the Singlet-Triplet Gap of Azaphenalenes

We analyze the possibility of symmetry-lowering induced by pseudo-Jahn--Teller interactions in six previously studied azaphenalenes that are known to have their first excited singlet state (S$_1$) lower in energy than the triplet state (T$_1$). The primary aim of this study is to explore whether Hund's rule violation is observed in these molecules when their structures are distorted from $C_{\rm 2v}$ or $D_{\rm 3h}$ point group symmetries by vibronic coupling. Along two interatomic distances connecting these point groups to their subgroups $C_{\rm s}$ or $C_{\rm 3h}$, we relaxed the other internal degrees of freedom and calculated two-dimensional potential energy subsurfaces. The many-body perturbation theory (MP2) suggests that the high-symmetry structures are the energy minima for all six systems. However, single-point energy calculations using the coupled-cluster method (CCSD(T)) indicate symmetry lowering in four cases. The singlet-triplet energy gap plotted on the potential energy surface also shows variations when deviating from high-symmetry structures. A full geometry optimization at the CCSD(T) level with the cc-pVTZ basis set reveals that the $D_{\rm 3h}$ structure of cyclazine (1AP) is a saddle point, connecting two equivalent minima of $C_{\rm 3h}$ symmetry undergoing rapid automerization. The combined effects of symmetry lowering and high-level corrections result in a nearly zero singlet-triplet gap for the $C_{\rm 3h}$ structure of cyclazine. Azaphenalenes containing nitrogen atoms at electron-deficient sites -- 2AP, 3AP, and 4AP -- exhibit more pronounced in-plane structural distortion; the effect is captured by the long-range exchange-interaction corrected DFT method, $ω$B97XD. Excited state calculations of these systems indicate that in their low-symmetry energy minima, T$_1$ is indeed lower in energy than S$_1$, upholding the validity of Hund's rule.

physics.chem-ph↗

arXiv2024

Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules

We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spectra of organic molecules originating from the $K$-shell ionization energies of carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed the training dataset through high-throughput calculations of $K$-shell core-electron binding energies (CEBEs) for 12,880 small organic molecules in the bigQM7$ω$ dataset, employing the $Δ$-SCF formalism coupled with meta-GGA-DFT and a variationally converged basis set. The models are cost-effective, as they require the atomic coordinates of a molecule generated using universal force fields while estimating the target-level CEBEs corresponding to DFT-level equilibrium geometry. We explore transfer learning by utilizing the atomic environment feature vectors learned using a graph neural network framework in kernel-ridge regression. Additionally, we enhance accuracy within the $Δ$-machine learning framework by leveraging inexpensive baseline spectra derived from Kohn--Sham eigenvalues. When applied to 208 combinatorially substituted uracil molecules larger than those in the training set, our analyses suggest that the models may not provide quantitatively accurate predictions of CEBEs but offer a strong linear correlation relevant for virtual high-throughput screening. We present the dataset and models as the Python module, ${\tt cebeconf}$, to facilitate further explorations.

physics.chem-ph↗

arXiv2024

Resilience of Hund's rule in the Chemical Space of Small Organic Molecules

We embark on a quest to identify small molecules in the chemical space that can potentially violate Hund's rule. Utilizing twelve TDDFT approximations and the ADC(2) many-body method, we report the energies of S$_1$ and T$_1$ excited states of 12,880 closed-shell organic molecules within the bigQM7$ω$ dataset with up to 7 CONF atoms. In this comprehensive dataset, none of the molecules, in their minimum energy geometry, exhibit a negative S$_1$-T$_1$ energy gap at the ADC($2$) level while several molecules display values $<0.1$ eV. The spin-component-scaled double-hybrid method, SCS-PBE-QIDH, demonstrates the best agreement with ADC(2). Yet, at this level, a few molecules with a strained $sp^3$-N center turn out as false-positives with the S$_1$ state lower in energy than T$_1$. We investigate a prototypical cage molecule with an energy gap $<-0.2$ eV, which a closer examination revealed as another false positive. We conclude that in the chemical space of small closed-shell organic molecules, it is possible to identify geometric and electronic structural features giving rise to S$_1$-T$_1$ degeneracy; still, there is no evidence of a negative gap. We share the dataset generated for this study as a module, to facilitate seamless molecular discovery through data mining.

physics.chem-ph↗

arXiv2023

Stereo-Electronic Factors Influencing the Stability of Hydroperoxyalkyl Radicals: Transferability of Chemical Trends across Hydrocarbons and ab initio Methods

The hydroperoxyalkyl radicals (.QOOH) are known to play a significant role in combustion and tropospheric processes, yet their direct spectroscopic detection remains challenging. In this study, we investigate molecular stereo-electronic effects influencing the kinetic and thermodynamic stability of a .QOOH along its formation path from the precursor, alkylperoxyl radical (ROO.), and the depletion path resulting in the formation of cyclic ether + .OH. We focus on reactive intermediates encountered in the oxidation of acyclic hydrocarbon radicals: ethyl, isopropyl, isobutyl, tert-butyl, neopentyl, and their alicyclic counterparts: cyclohexyl, cyclohexenyl, and cyclohexadienyl. We report reaction energies and barriers calculated with the highly accurate method Weizmann-1 (W1) for the channels: ROO. <=> .QOOH, ROO. <=> alkene + .OOH, .QOOH <=> alkene + .OOH, and .QOOH <=> cyclic ether + .OH. Using W1 results as a reference, we have systematically benchmarked the accuracy of popular density functional theory (DFT), composite thermochemistry methods, and an explicitly correlated coupled-cluster method. We ascertain inductive, resonance, and steric effects on the overall stability of .QOOH and computationally investigate the possibility of forming more stable species. With new reactions as test cases, we probe the capacity of various ab initio methods to yield quantitative insights on the elementary steps of combustion.

physics.chem-ph↗

arXiv2023

Band gaps of long-period polytypes of IV, IV-IV, and III-V semiconductors estimated with an Ising-type additivity model

We apply an Ising-type model to estimate the band gaps of the polytypes of group IV elements (C, Si, and Ge) and binary compounds of groups: IV-IV (SiC, GeC, and GeSi), and III-V (nitride, phosphide, and arsenide of B, Al, and Ga). The models use reference band gaps of the simplest polytypes comprising 2--6 bilayers calculated with the hybrid density functional approximation, HSE06. We report four models capable of estimating band gaps of nine polytypes containing 7 and 8 bilayers with an average error of $\lesssim0.05$ eV. We apply the best model with an error of $<0.04$ eV to predict the band gaps of 497 polytypes with up to 15 bilayers in the unit cell, providing a comprehensive view of the variation in the electronic structure with the degree of hexagonality of the crystal structure. Within our enumeration, we identify four rhombohedral polytypes of SiC -- 9$R$, 12$R$, 15$R$(1), and 15$R$(2) -- and perform detailed stability and band structure analysis. Of these, 15$R$(1) that has not been experimentally characterized has the widest band gap ($>3.4$ eV); phonon analysis and cohesive energy reveal 15$R$(1)-SiC to be metastable. Additionally, we model the energies of valence and conduction bands of the rhombohedral SiC phases at the high-symmetry points of the Brillouin zone and predict band structure characteristics around the Fermi level. The models presented in this study may aid in identifying polytypic phases suitable for various applications, such as the design of wide-gap materials, that are relevant to high-voltage applications. In particular, the method holds promise for forecasting electronic properties of long-period and ultra-long-period polytypes for which accurate first-principles modeling is computationally challenging.

physics.chem-ph↗

arXiv2023

Variational augmentation of Gaussian continuum basis sets for calculating atomic higher harmonic generation spectra

We present a variational augmentation procedure to optimize the exponents of Gaussian continuum basis sets for simulating strong-field laser ionization phenomena such as higher harmonic generation (HHG) in atoms and ions using the time-dependent configuration interaction (TDCI) method. We report the distribution of the optimized exponents and discuss how efficiently the resulting basis functions span the variational space to describe the near-continuum states involved in HHG. Further, we calculated the higher harmonic spectra of three two-electron systems -- H$^{-}$, He and Li$^{+}$ -- generated by 800nm driving laser-pulses with pulse-width of 54fs and peak intensities in the tunnel ionization regime of each system. We analyze the performance of these basis sets with an increasing number of higher angular momentum functions and show that up to $g$-type functions are required to obtain qualitatively accurate harmonic spectra. Additionally, we also comment on the impact of electron correlation on the HHG spectra. Finally, we show that by systematically augmenting additional shells we model the strong-field dynamics at higher laser peak intensities.

physics.chem-ph↗

arXiv2022

Understanding the role of intramolecular ion-pair interactions in conformational stability using an ab initio thermodynamic cycle

Intramolecular ion-pair interactions yield shape and functionality to many molecules. With proper orientation, these interactions overcome steric factors and are responsible for the compact structures of several peptides. In this study, we present a thermodynamic cycle based on isoelectronic and alchemical mutation to estimate intramolecular ion-pair interaction energy. We determine these energies for 26 benchmark molecules with common ion-pair combinations and compare them with results obtained using intramolecular symmetry-adapted perturbation theory. For systems with long linkers, the ion-pair energies evaluated using both approaches deviate by less than 2.5% in vacuum phase. The thermodynamic cycle based on density functional theory facilitates calculations of salt-bridge interactions in model tripeptides with continuum/microsolvation modeling, and four large peptides: 1EJG (crambin), 1BDK (bradykinin), 1L2Y (a mini-protein with a tryptophan cage), and 1SCO (a toxin from the scorpion venom).

physics.chem-ph↗

arXiv2022

Resolution-vs.-Accuracy Dilemma in Machine Learning Modeling of Electronic Excitation Spectra

In this study, we explore the potential of machine learning for modeling molecular electronic spectral intensities as a continuous function in a given wavelength range. Since presently available chemical space datasets provide excitation energies and corresponding oscillator strengths for only a few valence transitions, here, we present a new dataset -- \bigqm -- with 12,880 molecules containing up to 7 CONF atoms and report ground state and excited state properties. A publicly accessible web-based data-mining platform is presented to facilitate on-the-fly screening of several molecular properties including harmonic vibrational and electronic spectra. We present all singlet electronic transitions from the ground state calculated using the time-dependent density functional theory framework with the $ω$B97XD exchange-correlation functional and a diffuse-function augmented basis set. The resulting spectra predominantly span the X-ray to deep-UV region (10--120 nm). To compare the target spectra with predictions based on small basis sets, we bin spectral intensities and show good agreement is obtained only at the expense of the resolution. Compared to this, machine learning models with latest structural representations trained directly using $<10 \%$ of the target data recover the spectra of the remaining molecules with better accuracies at a desirable $<1$ nm wavelength resolution.

physics.chem-ph↗

arXiv2021

Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design

Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by +/- 1 eV. Time-dependent long-range corrected hybrid density functional methods can model the lowest excitation energies offering a semi-quantitative precision of +/- 0.3 eV. Alas, the chemical space of BODIPYs stemming from combinatorial introduction of -- even a few dozen -- substituents is too large for brute-force high-throughput modeling. To navigate this vast space, we select 77,412 molecules and train a kernel-based quantum machine learning model providing < 2% hold-out error. Further reuse of the results presented here to navigate the entire BODIPY universe comprising over 253 giga (253 x 10^9) molecules is demonstrated by inverse-designing candidates with desired target excitation energies.

physics.chem-ph↗

arXiv2021

Machine Learning Modeling of Materials with a Group-Subgroup Structure

Crystal structures connected by continuous phase transitions are linked through mathematical relations between crystallographic groups and their subgroups. In the present study, we introduce group-subgroup machine learning (GS-ML) and show that including materials with small unit cells in the training set decreases out-of-sample prediction errors for materials with large unit cells. GS-ML incurs the least training cost to reach 2-3% target accuracy compared to other ML approaches. Since available materials datasets are heterogeneous providing insufficient examples for realizing the group-subgroup structure, we present the "FriezeRMQ1D" dataset with 8393 Q1D organometallic materials uniformly distributed across 7 frieze groups. Furthermore, by comparing the performances of FCHL and 1-hot representations, we show GS-ML to capture subgroup information efficiently when the descriptor encodes structural information. The proposed approach is generic and extendable to symmetry abstractions such as spin-, valency-, or charge order.

physics.chem-ph↗