SOURCE-LINKED INTELLIGENCE

Machine-learning enabled design of multiferroic optoelectronic materials

CORDIS · observation · Publication date unknown

absence of scalable simulation tools that can capture coupled structural, electronic, and polarization responses across atomic to device length scales. The fellowship will establish physics-informed machine learning approaches that integrate latent Ewald summation and Hamiltonian-based models with first-principles calculations. This will enable accurate treatment of long-range electrostatics, electronic excitations, and ferroic switching dynamics. The methodology progresses from (i) developing advanced ML–DFT workflows, (ii) investigating domain wall dynamics, defect interactions, and their impact on local electronic structures, to (iii) scaling up to device-level simulations of charge transport and switching under realistic operating conditions. Iterative validation with experimental collaborators in spectroscopy and microscopy will ensure predictive accuracy and technological relevance. The expected outcomes include a validated, transferable open-source platform for modelling multiferroics, new insights into structure–property–function relationships at ferroic interfaces, and des

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recordType

award

status

SIGNED

region

EU

value

276187.92

unit

EUR