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Electronic Structures and Transition Properties of BeSe and BeTe Molecules.

ABSTRACT: The electronic structure of BeSe and BeTe molecules has been investigated using the ab initio CASSCF/(MRCI + Q) method at the spin-free and spin-orbit level. The potential energy curves, the permanent dipole moment, the spectroscopic constants T _e, R _e, ω_e, and B _e, and the dissociation energy D _e are determined in addition to the vertical transition energy T_v. The molecules' percentages of ionic character are deduced, and the trends of the spectroscopic constants of the two molecules are compared and justified. A ro-vibrational study is performed using the canonical function approach to calculate the constants E _v, B _v, and D _v and the turning points R _min and R _max. All the ground-state vibrational levels have also been investigated. The radiative lifetimes of vibrational transitions among the electronic ground states are also discussed. The results for BeSe have been compared with the previously published data while those for BeTe molecules are presented here for the first time.

SUBMITTER: Zeid I

PROVIDER: S-EPMC8603188 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe subtle trends in inorganic bonding and spin-state ordering, but these calculations are computationally costly and properties are sensitive to the exchange-correlation functional employed. To begin to overcome these challenges, we trained artificial neural networks (ANNs) to predict quantum-mechanically-derived properties, including spin-state ordering, sensitivity to Hartree-Fock exchange, and spin-state specific bond lengths in transition metal complexes. Our ANN is trained on a small set of inorganic-chemistry-appropriate empirical inputs that are both maximally transferable and do not require precise three-dimensional structural information for prediction. Using these descriptors, our ANN predicts spin-state splittings of single-site transition metal complexes (i.e., Cr-Ni) at arbitrary amounts of Hartree-Fock exchange to within 3 kcal mol-1 accuracy of DFT calculations. Our exchange-sensitivity ANN enables improved predictions on a diverse test set of experimentally-characterized transition metal complexes by extrapolation from semi-local DFT to hybrid DFT. The ANN also outperforms other machine learning models (i.e., support vector regression and kernel ridge regression), demonstrating particularly improved performance in transferability, as measured by prediction errors on the diverse test set. We establish the value of new uncertainty quantification tools to estimate ANN prediction uncertainty in computational chemistry, and we provide additional heuristics for identification of when a compound of interest is likely to be poorly predicted by the ANN. The ANNs developed in this work provide a strategy for screening transition metal complexes both with direct ANN prediction and with improved structure generation for validation with first principles simulation.

Dataset Information

Electronic Structures and Transition Properties of BeSe and BeTe Molecules.

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