136 papers
This paper critiques the 2025 i-DMFT method by Di Liu et al. for failing to accurately reproduce the Born-Oppenheimer potential energy curves of hydrogenic halides (HF, HCl, HBr) near equilibrium and for predicting qualitatively incorrect behavior in the long-range Van der Waals region that contradicts multipole expansion theory.
This paper introduces DOTA, a deep learning model that leverages local density of states to capture orbital interaction patterns, enabling accurate and efficient prediction of surface adsorption energies by aligning scarce experimental data with multi-fidelity quantum chemistry calculations.
This study introduces a non-empirical meta-generalized gradient approximation (meta-GGA) incorporating the Laplacian of the electron density that significantly mitigates self-interaction error in the one-electron system, yielding binding energy curves that outperform existing semilocal functionals like PBE and SCAN and closely match the exact solution.
This study demonstrates that incorporating quantum nuclear effects into ab-initio density functional calculations significantly improves the agreement between theoretical and experimental electron densities in crystalline LiH and LiD, revealing a breakdown of the strict Born-Oppenheimer approximation and notable temperature dependence in these light-element solids.
This paper introduces Variational Adaptive Gaussian Decomposition (VAGD), a scalable, quadrature-free framework that utilizes an autoencoder-decoder neural network to optimize Gaussian wave packet parameters, thereby enabling systematic improvements from thawed Gaussian approximations to full quantum mechanical dynamics through time-sliced semiclassical propagation.
This paper presents a unified Bayesian optimization framework using Gaussian processes with derivative observations and advanced extensions like Optimal Transport and random Fourier features to efficiently accelerate the search for minima and saddle points on potential energy surfaces, bridging theoretical formulation with practical implementation through accompanying Rust code.
This study demonstrates that Path Integral Molecular Dynamics simulations reveal nuclear quantum effects significantly accelerate the thermal decomposition of the TATB crystal and lower its activation energy by approximately 8% compared to classical methods, while highlighting that the Quantum Thermal Bath approximation substantially overestimates these quantum acceleration effects.
This paper presents the design and characterization of an "ultraslow optical centrifuge" capable of generating linearly polarized fields with arbitrarily low angular acceleration, demonstrating its tunability and successful application in spinning CS molecules for potential use in controlling molecular rotation within viscous media.
This study characterizes the reaction dynamics of O(3P) + O2(3Sigma_g-) collisions on a high-level MRCI+Q/aug-cc-pVQZ potential energy surface, revealing that while the computed rates and isotopic ratios capture experimental trends like negative temperature dependence and cusps, discrepancies in absolute values are primarily attributed to neglected quantum effects such as zero-point energy.
This paper introduces a universal, first-principles electronegativity scale based on the atomic mean inner potential (AMIP) that not only aligns with established trends and bonding classifications but also outperforms existing methods in predicting Lewis acid strengths and -ray annihilation spectral widths across diverse chemical systems.
This paper extends semi-classical instanton analysis to a symmetric quartic potential with four degenerate minima, deriving energy splittings and Rabi oscillations for distinct tunneling pathways that show excellent agreement with numerical results while revealing a critical coupling regime where the discrete symmetry melts into a continuous symmetry.
This paper derives rigorous quantum mechanical expressions in the electric-dipole approximation that establish generalized Einstein relations between absorption and emission spectra in dispersive media, linking dipole-strength spectra to Einstein coefficients through conditional transition probabilities and defining equilibrium Stokes' shifts via changes in standard chemical potential rather than simple degeneracy ratios.
This paper presents a scalable, two-step framework that propagates chemical kinetics parameter uncertainties onto reduced manifolds to enable efficient, spatially resolved uncertainty quantification in high-fidelity reacting flow simulations, revealing significant variability in trajectory and equilibrium times driven by mixing and low-to-intermediate temperature chemistry.
The paper introduces Geo-ADAPT-VQE, a geometry-aware adaptive variational quantum eigensolver that utilizes natural gradients for operator selection to achieve faster, more stable convergence and significantly shorter ansatz circuits compared to existing gradient-based methods.
This paper presents a computationally efficient theoretical framework based on the Power-Zienau-Woolley Hamiltonian and maximally localized Wannier functions that accurately captures light-matter interactions beyond the electric-dipole approximation in extended systems without requiring finite-order multipole expansions, thereby enabling precise first-principles simulations of spatially structured light dynamics in nanoscale materials.
This paper introduces an extension of localized intrinsic bond orbitals (IBOs) to decode correlated charge migration dynamics, successfully mapping complex many-electron behaviors to intuitive chemical concepts like curly arrows and hyperconjugation while identifying key mechanisms and efficient molecules through high-level TDDMRG simulations.
This paper reports the first successful search, observation, and coherent manipulation of electric-quadrupole forbidden vibrational transitions in single trapped nitrogen molecular ions (N) using quantum-logic spectroscopy, thereby overcoming the challenge of complex molecular energy structures to enable new applications in precision spectroscopy, molecular qubits, and infrared clocks.
This paper introduces LOREM, a graph neural network architecture that employs equivariant messages for long-range interactions to overcome the limitations of cutoff-based models in capturing non-local physical effects like electrostatics and electron delocalization, achieving consistent and superior performance across diverse datasets without requiring dataset-specific hyperparameter tuning.
The paper introduces SAFT-P, a plaquette-level extension of Statistical Associating Fluid Theory that improves the modeling of self-assembly in patchy colloids by retaining topology-dependent information and accurately predicting critical points and coexistence curves for particles with identical valence but different patch layouts.
This paper demonstrates that transforming molecular Hamiltonians to the adiabatic representation to eliminate spin-orbit coupling inadvertently generates significant non-adiabatic couplings that must be explicitly included to avoid severe errors in rovibronic predictions, providing exact conditions for validity and practical diagnostics for when single-state approximations fail.