Journal of Biochemistry Research

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 Abstract As machine learning (ML) has matured, by offering the promise of simultaneous paradigm shifts in accuracy and efficiency it's opened a replacement frontier in theoretical and computational chemistry. Nowhere is that this advance more needed, but also tougher to realize , than within the discovery of open‐shell transition metal complexes. Here, localized d or f electrons exhibit variable bonding that's challenging to capture even with the foremost computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models which will supplement or maybe replace explicit electronic structure calculations. In this article,  including the power to approach sub‐kcal/mol accuracy on a variety of properties with tailored representations,I outline the recent advances in building ML models in transition metal chemistry, to get and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that are essential to enabling ML in open‐shell transition metal chemistry, including  (a) the importance of quantitative assessments of both theory and model domain of applicability, and (b) the necessity to enable autonomous generation of reliable, large data sets both for ML model training and in active learning or discovery contexts (c) the connection of knowledge set size/diversity, model complexity, and representation choice,. Finally, I summarize subsequent steps toward making ML a mainstream tool within the accelerated discovery of transition metal complexes.  

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