The Gagliardi Group addresses the most compelling challenges of our planet related to clean energy.
Research Subjects
We develop novel quantum chemical methods and apply them to study phenomena related to sustainable energies. We are interested in modeling molecular species, materials, and interfaces. We develop electronic structure theories, machine learning protocols, and combine quantum and classical simulation techniques.
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We model actinide and transactinide chemistry, with the aim of understanding the electronic structure and chemical bonding of molecular species both in their ground and excited states.
We model catalysis, spectroscopy and photochemistry of molecular systems containing transition metals and catalytic phenomena involving metal or metal-oxide clusters attached to a support, such as metal-organic frameworks (MOFs).
Classical force field-based simulation methods overcome the system size limitations of quantum chemical simulation methods to model several phenomenal.
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We develop electronic structure methods to model strongly correlated systems, including organic compounds, transition-metal, actinide and lanthanide complexes, and qubit candidates.
We use machine learning algorithms to parameterize models to make useful predictions from theoretical and experimental data.
Metal-organic frameworks (MOFs) are crystalline, porous, extended structures, which link transition metal ions by anionic organic linkers.
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We develop electronic structure protocols to study magnetic properties of molecular systems and materials and to assess their potential as spin-qubits.
Quantum computers are inherently powerful tools in quantum chemistry because of their ability to prepare superpositions of states and perform unitary transformations.
Photoinduced physical and chemical processes in molecules are central to many applications in the chemical industry including photoredox catalysis, organic synthesis, and design of photosensitizers and photoluminescent materials.
What We Do
We develop and employ advanced quantum and classical simulations as well as data science to discover and understand the next generation of chemical systems and materials: catalysts that are more sustainable, photovoltaics that are more efficient, and qubits that are more reliable.
News
Matthew will use it to attend the 17th INTERNATIONAL CONGRESS OF QUANTUM CHEMISTRY. https://icqc2023.org Where he will present a poster on his research on linearized pair-density functional theory. Way to Continue reading Matthew Hennefarth has received the Olshansky Graduate Student Travel Award.
Daniel studies the automation of multiconfigurational methods for high-throughput computation, and uses this data to further computational methodologies with data science and machine learning. He also works with experimentalists to Continue reading Daniel King receives the Eugene Olshansky Memorial Fellowship for Excellence in Research
Mukunda Mandal has joined the group as a postdoc, most recently a postdoc at Max Planck Institute for Polymer Research, Germany He is interested in Electronic Structure, Catalysis, and Reticular Continue reading Dr. Mukunda Mandal has joined the Gagliardi Group!