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Δ -Machine Learning for the Prediction of Metal Complex Properties

The discovery and design of novel transition metal complexes for specific applications heavily rely on computational high-throughput screenings to identify promising candidates for experimental validation. However, traditional computational approaches, such as density functional theory, are often too computationally demanding to be applied on a large scale. Machine learning methods offer a promising alternative due to their excellent computational efficiency, but their accuracy and high data requirements remain…