Predicting Catalyst Selectivity

At its core, the raison d’être of chemistry is to understand the connection between the molecular and macroscopic worlds. Molecular composition dictates all manifestations of the physical properties of matter and its ability to undergo changes. Accordingly, for centuries, chemists have been preoccupied with learning how molecular structure relates to function, be it an antibiotic, a fragrance, a flame retardant, a pigment, or a vitamin, essentially, every natural and man-made object on Earth. A corollary to this endeavor is to learn how to manipulate molecular structure with exquisite precision to optimize the performance of the macroscopic material. One of the most spectacular triumphs of modern synthetic chemistry is the ability to make and modify molecules with surgical precision at the atomic level. This accomplishment is enabled by the invention of highly specialized reagents and catalysts that can break and forge new chemical bonds with a stunning level of selectivity in the face of many possibilities. However, the development of these almost “magical” agents is an extremely challenging enterprise that, still in the 21st century, relies on Edisonian empiricism. Through often arduous and time-consuming trial and error, even the most proficient experts can take years to invent a new reagent or catalyst for a specific operation.  

With support from the W. M. Keck Foundation, the Denmark laboratory has developed a new, computationally-guided workflow that can accelerate the identification and optimization of catalysts. A critical feature of this new approach is to create a virtual library of thousands of hypothetical catalyst structures that encompass an enormous diversity of molecular properties. By generating an extremely diverse library, we maximize the probability of creating one or more structures which will have the ideal properties to effect the desired transformation with optimum performance. The challenge, however, is how to find the needle in that haystack. To do this, we identify a subset of library members that represent the chemical diversity to the greatest extent possible, much in the way our 535 members of the Congress represent the 350 million citizens of the USA. The “representatives” constitute a “training set” from which we collect experimental data on a given transformation. The data from this training set is then used as input into a machine learning algorithm, which can in turn can find patterns in large amounts of information that are beyond the cognitive limit of humans. The mathematical equations generated by the algorithms then are used to predict the behavior of each catalyst in the virtual library. Thus, it is possible to select the best catalyst for the transformation under study. In Their seminal publication, the Denmark lab demonstrated the power in this approach by using a set of non-optimal data to predict optimal catalysts for a test reaction. The model accurately ranked the catalysts in order of their selectivity, successfully simulating the optimization of a heretofore unoptimized reaction. After a decade of failures, this accomplishment represents a successful culmination and harbinger of exciting avenues to pursue for decades to come. 

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