The accurate description of large molecular systems has triggered the development of new computational methods. Due to the computational cost of modeling large systems, the methods usually require a trade-off between accuracy and speed. Therefore, benchmarking to test the accuracy and precision of the method is an important step in their development. The typical gold standard for evaluating these methods is isolated molecules, because of the low computational cost. However, the advent of high-performance computing has made it possible to benchmark computational methods using observables from more complex systems such as liquid solutions. To this end, infrared spectroscopy provides a suitable set of observables (i.e., vibrational transitions) for liquid systems. Here, IR spectroscopy observables are used to benchmark the predictions of the newly developed GFN2-xTB semiempirical method. Three different IR probes (i.e., N-methylacetamide, benzonitrile, and semiheavy water) in solution are selected for this purpose. The work presented here shows that GFN2-xTB predicts central frequencies with errors of less than 10% in all probes. In addition, the method captures detailed properties of the molecular environment such as weak interactions. Finally, the GFN2-xTB correctly assesses the vibrational solvatochromism for N-methylacetamide and semiheavy water but does not have the accuracy needed to properly describe benzonitrile. Overall, the results indicate not only that GFN2-xTB can be used to predict the central frequencies and their dependence on the molecular environment with reasonable accuracy but also that IR spectroscopy data of liquid solutions provide a suitable set of observables for the benchmarking of computational methods.
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Quantum dynamics using path integral coarse-graining
The vibrational spectra of condensed and gas-phase systems are influenced by thequantum-mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum dynamics are possible thanks to the imaginary time path-integral (PI) formulation of quantum statistical mechanics, albeit at a high computational cost which increases sharply with decreasing temperature. By leveraging advances in machine-learned coarse-graining, we develop a PI method with the reduced computational cost of a classical simulation. We also propose a simple temperature elevation scheme to significantly attenuate the artifacts of standard PI approaches as well as eliminate the unfavorable temperature scaling of the computational cost. We illustrate the approach, by calculating vibrational spectra using standard models of water molecules and bulk water, demonstrating significant computational savings and dramatically improved accuracy compared to more expensive reference approaches. Our simple, efficient, and accurate method has prospects for routine calculations of vibrational spectra for a wide range of molecular systems - with an explicit treatment of the quantum nature of nuclei.
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- Award ID(s):
- 2019745
- NSF-PAR ID:
- 10416928
- Date Published:
- Journal Name:
- The Journal of Chemical Physics
- Volume:
- 157
- Issue:
- 18
- ISSN:
- 0021-9606
- Page Range / eLocation ID:
- 181102
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accurately modeling absorption and fluorescence spectra for molecules in solution poses a challenge due to the need to incorporate both vibronic and environmental effects, as well as the necessity of accurate excited state electronic structure calculations. Nuclear ensemble approaches capture explicit environmental effects, Franck–Condon methods capture vibronic effects, and recently introduced ensemble-Franck–Condon approaches combine the advantages of both methods. In this study, we present and analyze simulated absorption and fluorescence spectra generated with combined ensemble-Franck–Condon approaches for three chromophore–solvent systems and compare them to standard ensemble and Franck–Condon spectra, as well as to the experiment. Employing configurations obtained from ground and excited state ab initio molecular dynamics, three combined ensemble-Franck–Condon approaches are directly compared to each other to assess the accuracy and relative computational time. We find that the approach employing an average finite-temperature Franck–Condon line shape generates spectra nearly identical to the direct summation of an ensemble of Franck–Condon spectra at one-fourth of the computational cost. We analyze how the spectral simulation method, as well as the level of electronic structure theory, affects spectral line shapes and associated Stokes shifts for 7-nitrobenz-2-oxa-1,3-diazol-4-yl and Nile red in dimethyl sulfoxide and 7-methoxy coumarin-4-acetic acid in methanol. For the first time, our studies show the capability of combined ensemble-Franck–Condon methods for both absorption and fluorescence spectroscopy and provide a powerful tool for simulating linear optical spectra.
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Abstract High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence–quantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C 60 ) close to experiment. This opens an opportunity to investigate chemical compounds with previously unattainable speed and accuracy as we demonstrate by determining geometries of polyyne molecules—the task difficult for both experiment and theory. Noteworthy, our method’s accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0120386
