WIRES Outlook on the future of ML

As machine learning 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 where localized d or f electrons exhibit variable bonding that is challenging to capture even with the most computationally demanding methods. Thus, despite great promise, clear obstacles remain in constructing machine learning models that can supplement or even replace explicit electronic structure calculations. In this Opinion, I outline the recent advances in building machine learning (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 i) the relationship of data set size/diversity, model complexity, and representation choice, ii) the importance of quantitative assessments of both theory and model domain of applicability, and iii) 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 machine learning a mainstream tool in the accelerated discovery of transition metal complexes.

Check out the WIRES invited Opinion summarizing our group's outlook on making machine learning a useful tool in the accelerated discovery of transition metal complexes here!

About Us

The Kulik group focuses on the development and application of new electronic structure methods and atomistic simulations tools in the broad area of catalysis.

Our Interests

We are interested in transition metal chemistry, with applications from biological systems (i.e. enzymes) to nonbiological applications in surface science and molecular catalysis.

Our Focus

A key focus of our group is to understand mechanistic features of complex catalysts and to facilitate and develop tools for computationally driven design.

Contact Us

Questions or comments? Let us know! Contact Dr. Kulik: