Infrared spectra of neutral polycyclic aromatic hydrocarbons based on machine learning potential energy surface and dipole mapping

Laurens, Gaétan; Rabary, Malalatiana; Lam, Julien; Peláez, Daniel; Allouche, Abdul‐Rahman

doi:10.1007/s00214-021-02773-6

Cited by 12 publications

(9 citation statements)

References 88 publications

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“…Such models combine the extensive, accurate, finite-temperature thermodynamic sampling afforded by an ML potential, as in a series of the previous works 45,47,49,50,57,58,60,77,78 , with the expressiveness and utility of an ML property model. Particularly relevant are the studies using a potential energy surface combined with a dipole-moment model for studying the infrared spectra of isolated molecules 79,80 . To date, such combined models have not yet been applied to ferroelectric materials; one important difficulty for ML modeling is the multi-valued character of the polarization in the condensed phase (although see ref.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

et al. 2022

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Modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning model that describes on the same footing structural, energetic, and functional properties of barium titanate (BaTiO3), a prototypical ferroelectric. The model uses ab initio calculations as a reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. These predictions allow us to assess the microscopic mechanism of the ferroelectric transition. The presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition, even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate, partly-ordered phases. Moreover, we thoroughly probe the static and dynamical behavior of BaTiO3 across its phase diagram without the need to introduce a coarse-grained description of the ferroelectric transition. Finally, we apply the polarization model to calculate the dielectric response properties of the material in a full ab initio manner, again reproducing the correct qualitative experimental behavior.

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Section: Introductionmentioning

confidence: 99%

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

et al. 2022

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“…Evidence of the success of these methods can be seen through the very diverse types of materials that have been so far modeled with MLIP, including metals, oxides, carbon and silicon-based organics, and perovskites. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44] Prompted by these advances, DFT accurate large scale simulations can finally be carried out to investigate the intricate formation processes occurring in nanoparticle synthesis.…”

Section: Introductionmentioning

confidence: 99%

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

Gromoff,

Benzo,

Saidi

et al. 2024

Nanoscale

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“…PAHs and their derivatives are significant to hydrocarbon resources, − combustion, − astrophysics, − organic semiconductors, − and carbonaceous nanomaterials. Even the simplest class of aromatic structures–the benzenoid hydrocarbons–belie deep complexities, with variations in geometry and size tuning a plethora of novel emergent physical properties relevant to technological applications (magnetic, optical, electronic, chemical), which have garnered much interest. − While interest in these novel properties has accelerated, there are holes in the comprehensive understanding of basic properties. For instance, a systematic overview of PAH emission energies is currently lacking.…”

Section: Introductionmentioning

confidence: 99%

Size Isn’t Everything: Geometric Tuning in Polycyclic Aromatic Hydrocarbons and Its Implications for Carbon Nanodots

Scott,

Dale,

McBroom

et al. 2024

J. Phys. Chem. A

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Recent developments in light-emitting carbon nanodots and molecular organic semiconductors have seen renewed interest in the properties of polycyclic aromatic hydrocarbons (PAHs) as a family. The networks of delocalized π electrons in sp 2 -hybridized carbon grant PAHs light-emissive properties right across the visible spectrum. However, the mechanistic understanding of their emission energy has been limited due to the ground state-focused methods of determination. This computational chemistry work, therefore, seeks to validate existing rules and elucidate new features and characteristics of PAHs that influence their emissions. Predictions based on (timedependent) density functional theory account for the full 3dimensional electronic structure of ground and excited states and reveal that twisting and near-degeneracies strongly influence emission spectra and may therefore be used to tune the color of PAHs and, hence, carbon nanodots. We particularly note that the influence of twisting goes beyond torsional destabilization of the groundstate and geometric relaxation of the excited state, with a third contribution associated with the electric transition dipole. Symmetries and peri-condensation may also have an effect, but this could not be statistically confirmed. In pursuing this goal, we demonstrate that with minimal changes to molecular size, the entire visible spectrum may be spanned by geometric modification alone; we have also provided a first estimate of emission energy for 35 molecules currently lacking published emission spectra as well as clear guidelines for when more sophisticated computational techniques are required to predict the properties of PAHs accurately.

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Infrared spectra of neutral polycyclic aromatic hydrocarbons based on machine learning potential energy surface and dipole mapping

Cited by 12 publications

References 88 publications

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

Size Isn’t Everything: Geometric Tuning in Polycyclic Aromatic Hydrocarbons and Its Implications for Carbon Nanodots

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