The Momeni Group is a multifaceted research team interested in developing novel quantum dynamics methodologies, parallel software, and AI/ML-driven materials design and discovery to address the most challenging problems related to renewable energy generation and energy storage. We study the dynamics and spectroscopy of systems ranging from molecules to materials and different interfaces, including organic molecular crystals, inorganic, and hybrid organic-inorganic materials.

Quantum Dynamics Method and Software Developments

Quantum Dynamics Method and Software Developments

We develop novel path integral (PI) method with special focus on both accuracy and efficiency for atomistic simulations of quantum dynamical properties and vibrational spectra. Our new real-time PI methods solve the inherent problems associated with extended ring polymers at lower temperatures and for quantum nuclei. Our developed new methodologies are implemented and freely shared via our open-source general-purpose MD software DL_POLY Quantum.

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Light-Matter Interactions

Light-Matter Interactions

Molecular-level understanding of solid-solution interfaces of electrochemical reactions involving proton/ion transport processes has long fascinated the research community. Studying light-matter interactions in the infrared region can provide a swath of information about these complex processes. We are interested in developing new path integral methods that are capable of addressing inherent problems associated with approximate legacy real-time path integral methods such as the infamous curvature problem of CMD for the accurate prediction of the vibrational spectra.

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Data-Driven Materials discovery & Design

Data-Driven Materials discovery & Design

We are broadly interested in adsorptive separation as well as energy conversion and storage and electrocatalytic processes involving redox-active intrinsically porous materials. Our primary objectives are: (i) to gain fundamental insights into structure-property-function relationships in different classes of nanoporous materials for the desired applications and (ii) to develop and employ data-driven algorithms for the accelerated inverse design and discovery of novel materials with superior properties and functions.

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