Abstract
Atomic clusters, with their varied size, unique properties, and distinct reactivity patterns are of tremendous use in environmental, biological, chemical and industrial domains. Machine learning (ML)-based algorithms are explored in obtaining their global minimum (GM) energy structures. Some important characteristics of these clusters include confinement-induced reactivity in small molecules, aromatic stabilization, improved superhalogen and superalkali properties, bond-stretch isomerism, planar hypercoordinate carbon, electride characteristics, hydrogen storage, Renner-Teller effect, to name a few. Confinement-induced changes are studied in the electronic structure of the guest molecules, their bonding and reactivity, increased bonding interaction between two noble gas atoms, improved rate of a chemical reaction, etc. Some of them are potentially efficient hydrogen storage and transport materials. Some are reported to exhibit fluxional behavior as well. This chapter intends to highlight all these aspects.
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