SCOML · Machine learning methods for excited-state dynamics simulations in light-induced spin-crossover complexes

Machine learning methods for excited-state dynamics simulations in light-induced spin-crossover complexes

Spin-crossover complexes are transition-metal coordination compounds characterized by their ability to switch magnetic properties in response to external stimuli such as light, temperature, magnetic fields, or pressure. In Fe(II)-based spin-crossover compounds, the transition occurs between the low-spin singlet state and the high-spin quintet state Within the SCOML project, we aim to investigate light-induced processes in which the system is initially prepared in its low-spin state and then excited to high-energy singlet states. Following excitation, the system undergoes a relaxation pathway involving ultrafast, radiationless (vibronically driven) transitions that ultimately populate the high-spin state. At sufficiently low temperatures, the system can become trapped in this metastable high-spin state, leading to a measurable change in total magnetization. This phenomenon is known as Light-Induced Excited Spin-State Trapping (LIESST). Such complexes hold great promise as molecular switches in emerging spintronics and photonics technologies. Despite significant experimental advances in characterizing these materials, computational approaches still face major challenges in providing a quantitative description of the light-induced spin-crossover mechanism. The main difficulty lies in the high computational cost of accurately simulating excited-state dynamics in medium-sized transition-metal complexes. This is due to the breakdown of the Born–Oppenheimer approximation in ultrafast radiationless processes, combined with the need to explicitly account for vibronic coupling between electronic potential energy surfaces. These requirements ultimately translate into an extremely large number of ab initio electronic structure single-point calculations. As a result, this class of systems remains largely unexplored from a computational perspective, leaving experimental efforts without robust quantitative tools to rationalize the observed relaxation pathways. This lack of synergy hampers the systematic improvement of material performance. The central objective of the SCOML project is therefore to establish a proof of concept for a novel machine-learning-based strategy designed to enable the systematic exploration of spin-crossover materials and the identification of new compounds with improved properties.

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