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An Algorithm Predicting the Optimal Mechanical Response of Electronic Energy Difference.

Alejandro JodraCristina García-IriepaLuis Manuel Frutos
Published in: Journal of chemical theory and computation (2023)
The use of mechanical forces at the molecular level has been shown to be an interesting tool for modulating different chemical and physical molecular properties. The so-called covalent mechanochemistry deals with the application of precise mechanical forces that induce specific changes in the structure, stability, reactivity, and other physical properties. The use of this kind of force to modulate photophysical properties and photochemical reactivity has also been studied. Nevertheless, the general problem of mechanical modulation of the energy gap between two electronic states has been addressed only with the development of simple theoretical models. Here, we develop and implement an algorithm providing the Largest energy Gap variation with Minimal mechanical Force (LGMF) that allows the determination of the optimal mechanical forces tuning the electronic energy gap, as well as to identify the maximum mechanical response of a molecular system to the application of any mechanical stimulus. The algorithm has been implemented for diverse molecular systems showing different degrees of flexibility. The phyton code of the algorithm is available in a public repository.
Keyphrases
  • machine learning
  • single molecule
  • deep learning
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