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Abstract While the impact of machine learning (ML) has been felt everywhere, its effect has been most transformative where large, high-quality datasets are available. For promising materials spaces, such as transition metal coordination complexes and metal–organic frameworks, the large chemical diversity has not yet been matched by similarly large datasets, and computational datasets (e.g., from density functional theory) may not be predictive. Extraction of experimental data from the literature represents an alternative approach to the data-driven design of materials. This perspective will describe efforts in (i) extracting experimental data; (ii) associating extracted data with known chemical structures; (iii) leveraging data in ML and screening; (iv) designing materials with enriched stability; and (v) using experimental data to improve high-throughput workflows. I will summarize some of the outstanding challenges and opportunities for data enrichment with high-throughput experimentation and large language models. Graphical abstract 

Abstract Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry. 

Abstract As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise of simultaneous paradigm shifts in accuracy and efficiency. Nowhere is this advance more needed, but also more challenging to achieve, than in the discovery of open‐shell transition metal complexes. Here, localizeddorfelectrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing ML models that can supplement or even replace explicit electronic structure calculations. In this article, I outline the recent advances in building ML models in transition metal chemistry, including the ability to approach sub‐kcal/mol accuracy on a range of properties with tailored representations, to discover and enumerate complexes in large chemical spaces, and to reveal opportunities for design through analysis of feature importance. I discuss unique considerations that have been essential to enabling ML in open‐shell transition metal chemistry, including (a) the relationship of data set size/diversity, model complexity, and representation choice, (b) the importance of quantitative assessments of both theory and model domain of applicability, and (c) the need to enable autonomous generation of reliable, large data sets both for ML model training and in active learning or discovery contexts. Finally, I summarize the next steps toward making ML a mainstream tool in the accelerated discovery of transition metal complexes. This article is categorized under: Electronic Structure Theory > Density Functional Theory Software > Molecular Modeling Computer and Information Science > Chemoinformatics 

Leveraging natural language processing models including transformers, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism.