Giovanni Li Manni

We develop next-generation electronic structure methods for the predictive simulation of systems with many unpaired electrons. These strongly correlated systems underpin catalysis, molecular magnetism, and photophysics, yet remain beyond the reach of conventional approaches.

We advance multiconfigurational electronic structure theory into a scalable framework by combining wave function methods, perturbation theory, multi-configuration pair-density functional theory, and stochastic algorithms. This enables accurate treatments of large active spaces without sacrificing essential correlation effects, further enhanced by metaheuristic strategies such as genetic algorithms.

Spin is our guiding principle. We resolve magnetic interactions, excited states, and reactivity across full potential energy surfaces, including spin–orbit coupling, environmental effects, and finite-temperature properties.

We target polynuclear transition-metal clusters, biomimetic catalysts, and correlated materials, contributing to emerging technological directions. Our work supports the design of molecular spin qubits, photoactive materials, and catalysts for sustainable transformations such as hydrogen and ammonia production.

Representative systems include [FeS] cluster models involved in electron transfer and nitrogen fixation, and water-splitting catalysts inspired by the CaMn₄O₅ center in photosystem II. Single-molecule magnets and cluster models of crystalline materials are also targets of our investigations. These studies deliver both mechanistic insight and quantitative predictions.

We integrate theory with experiment to guide synthesis and spectroscopy while continuously refining our models. In parallel, we contribute to emerging technologies, from quantum computing to functional materials design. Our goal is to establish a predictive, scalable platform for strongly correlated chemistry while training researchers at the interface of chemistry, physics, and computation.