Molecular Magnetism: Predicting Magnetic Properties with Modern Computational Methods

Dear Colleagues,

To rationally design novel molecular magnetic materials, synthetic chemists need robust computational tools to screen prospective molecular building blocks and assembly strategies before synthesis. Such preliminary assessment is crucial and can be executed via complementary theoretical approaches.

One powerful strategy is data-driven prediction, which utilizes spatial–electronic cooperativity via machine learning. This inherently bottom-up approach mines complex, often non-intuitive relationships between a molecule's spatial structure and its emergent electronic properties to identify promising starting molecular objects. Conversely, the same computational paradigm facilitates a top-down design framework by predicting the properties of complex, multidimensional magnetic systems for the rational selection of optimal molecular constituents.

At present, computational methods have achieved a level of accuracy sufficient for reliably modeling the magnetic behavior of synthesized single-molecule magnets (SMMs). These SMMs exhibit slow magnetization relaxation, a property stemming from magnetic anisotropy that creates an energy barrier (Ueff) between magnetic states with opposite sign of spin or total angular momentum projections. As the key challenges in developing quantum magnets operational above liquid nitrogen temperature are increasing this energy barrier, suppressing the quantum tunneling of magnetization (QTM) and avoiding strong spin-phonon coupling with low-frequency phonons, the preliminary theoretical “screening” of the electronic and magnetic parameters of candidate molecular systems is of critical importance.

This Special Issue of Magnetochemistry surveys recent progress in the theoretical design of molecular systems with quantum magnetic properties, exploring them as both isolated quantum units and components in multifunctional architectures.

Dr. Kira E. Vostrikova
Dr. Alexey A. Dmitriev
Prof. Dr. Marius Andruh
Guest Editors

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• data-driven chemistry
• machine learning (ML)
• magnetic behavior prediction
• theoretical computations of molecular magnets
• magnetic anisotropy analysis
• ab initio calculations