2025/02/13 by Atreyee Majumdar, Majumdar, Atreyee, Raghunathan Ramakrishnan +1 · 1 citation
Chemistry · Physics and Astronomy · #Advanced Physical and Chemical Molecular Interactions #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Quantum and electron transport phenomena #Quantum, superfluid, helium dynamics
paper · pdf · doi:10.48550/arxiv.2502.09330
openalex publication_date 2025/02/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Molecules that have been suggested to violate the Hund's rule, having a first excited singlet state (S1) energetically below the triplet state (T1), 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 S1-T1 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 (≈10 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.