Computational and Machine‐Learning Studies of Ethylene Oligomerization

ABSTRACT Linear alpha olefins (LAOs) are important petrochemical precursors for the synthesis of polymers, lubricants, plasticizers, and detergents. Currently, LAOs are usually synthesized via ethylene oligomerization using transition metal catalysts. Stimulated by the industrial advances, the questions on the reaction mechanism, origins of reactivity and selectivity have emerged, as well the need of rational design of catalysts. In this context, computational and machine‐learning studies not only elucidate the experimental observations from an energetic and molecular perspective but also provide essential insights into the structure–property relationship and the design of catalysts in the oligomerization. This review focuses on recent advances in computational and machine‐learning studies of ethylene oligomerization, highlighting mainstream catalyst systems based on Co, Ta, Ti, Zr, and Hf, with particular emphasis on Fe‐ and Cr‐based catalysts and their controlling factors governing reactivity and LAO distribution.