SOURCE-LINKED INTELLIGENCE

Predictive algorithms for simulating quantum materials

CORDIS · observation · Publication date unknown

lset. The project will focus on combining field-theory based methods for both perturbative and non-perturbative ab-initio and model systems with innovations in tensor techniques, quantum Monte Carlo, machine learning, and numerical analysis. By utilizing these innovative methods, we aim to deepen our understanding of quantum phases and exotic properties of materials, focusing in particular on experimentally measurable quantities. Currently, accurate methods for studying correlated quantum materials and their excitations are lacking. Established technology either employs the so-called density functional theory, which relies on uncontrolled approximations to electron correlations and may be imprecise for systems with partially filled d- or f-shells, or proceeds by downfolding to an effective low-energy model which may capture correlations but neglects import aspects of electronic structure. Recent years have seen substantial progress in methodologies for simulating finite-temperature field theories ab-initio, using diagrammatic perturbation theory and non-perturbative embedding method

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recordType

award

status

SIGNED

region

EU

value

3499299

unit

EUR