From the discovery of the Ziegler Natta polymerization catalyst, which underpins modern synthetic materials, to the discovery of the Haber-Bosch synthesis of ammonia, which enables the large-scale production of fertilizer essential to global food production, organometallic chemistry and catalysis are two of the main drivers of human innovation.
The advent of enantioselective catalysis utilizing transition metal complexes has revolutionized organic synthesis enabling the efficient construction of complex organic molecules with unprecedented efficiency. The general principles governing enantioinduction and catalytic activity for a wide range of important catalyst structures and reactions have been established thanks to the pioneering work of luminaries in the field of asymmetric catalysis such as Knowles, Noyori, and Sharpless as well as the monumental contributions of Heck, Negishi, and Suzuki, in the field of catalytic cross-coupling reactions utilizing transition metal complexes.
Thanks to the large number of powerful chemical transformations, which have been developed using transition metal complexes, the field of synthetic chemistry has largely moved beyond the question of whether any particular molecular structure can be made, to the pursuit of developing methods that enable structures to be prepared with increasing efficiency and stereocontrol, utilizing readily available starting materials.
In this vein, the development of enantioselective multicomponent coupling reactions that enable diverse product motifs to be prepared efficiently, in a modular fashion from simple and readily available chemical building blocks will increasingly drive innovation at the forefront of modern organometallic chemistry and catalysis.
Engaging renewable chemical resources is vitally important as increasing global energy demands decrease available hydrocarbon-based materials. Yet, renewable chemical feedstocks, such as phenols, are chronically underutilized due to the mechanistic challenge of engaging these often less-reactive compounds in pharmaceutical and industrial processes designed around the use of non-renewable reagents. Developing new catalytic processes specifically designed to engage abundant and inexpensive renewable reagents will provide an arena in which to craft innovative approaches to challenging problems of measurable and imminent importance to society.
I anticipate that the promise this area of research holds will be proportional to the challenges the organometallic chemistry community will encounter in its pursuit. It will become increasingly necessary to combine the principles of rate acceleration and stereocontrol that have been traditionally segregated into the separate domains of organometallic synthesis and transition metal catalysis with those of enzymatic- and organocatalysis. The design of new catalysts and cooperative multi-catalyst systems, which combine the potent reactivities of transition metal complexes with the principles underpinning rate acceleration and enantiocontrol in non-covalent organocatalysis, will become increasingly important.
Discoveries in merging organometallic chemistry with photoredox catalysis and electrochemistry as pioneered by Macmillan, Baran, and others will become increasingly prominent and have a lasting impact on organic synthesis.
In addition to advances in these areas, discoveries in the merging of machine learning with large chemical data sets will likely fundamentally reshape the world in the coming century. Organic and organometallic reactions, which, in contrast to enzymatic and organocatalytic reactions, often involve well-defined covalent interactions and discreet intermediates of moderate size, will likely serve as a training ground for this emerging technology and be the earliest domain of chemistry to substantially benefit from these advances. Researchers such as Sigman have pioneered merging parameterized data sets with computational predictive modeling. Machine learning can harness data sets generated by industry and academia to generate programs that can accurately interpret and predict chemical reactions. This has implications for improving reaction discovery, planning chemical routes, and guiding reaction optimization, fundamentally improving all areas affected by chemistry.