Publications by authors named "Jacob J Wardzala"

Excited states of transition metal complexes are generally strongly correlated due to the near-degeneracy of the metal d orbitals. Consequently, electronic structure calculations of such species often necessitate multireference approaches. However, widespread use of multireference methods is hindered due to the active space selection problem, which has historically required system-specific chemical knowledge and a trial-and-error approach.

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In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g.

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Article Synopsis
  • - The UiO-6x family of metal-organic frameworks is being studied for their potential to capture and destroy chemical warfare agents (CWAs), with a focus on understanding diffusion processes to improve material effectiveness.
  • - Due to the large size of CWAs, direct molecular simulations are challenging with pristine UiO-66, so isopropanol (IPA) is used as a model molecule to investigate diffusion mechanisms in the framework.
  • - The study reveals that hydrogen bonding interactions significantly affect the diffusion of IPA in UiO-66, leading to a substantial decrease in diffusion rates and showing a mixed behavior in mobility among IPA molecules during simulations.
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Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal-organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach.

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