Experimental Validation of Ferromagnetic–Antiferromagnetic Competition in Fe_xZn_1–xSe Quantum Dots by Computational Modeling

Traditionally computational methods have been employed to explain the observation of novel properties in materials. The use of computational models to anticipate the onset of such properties in quantum dots (QDs) a priori of their synthetic preparation would facilitate the rapid development of new materials. We demonstrate that the use of computational modeling can allow the design of magnetic semiconductor QDs based on iron doped ZnSe prior to the preparation of the sample. DFT modeling predicts the formation of multinuclear Fe clusters within the 10% Fe doped ZnSe QD to relieve lattice strain leading to the onset of competing ferromagnetic (FM)–antiferromagnetic (AFM) interactions, or in effect spin frustration, between the local spins. The magnetic properties when iron is incorporated into a 1.8 nm ZnSe QD are computationally analyzed using standard density functional theory (DFT) simulations, and the resultant spin and Fe localization models are experimentally evaluated using SQUID, ⁵⁷Fe Mössbauer, and electron paramagnetic resonance (EPR) spectroscopy. The observation that the experimental results agree with the DFT predicted behavior demonstrates the value of using modeling when targeting a desired material property.