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Abstract Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO₂concentrations. On an industrial scale, amine‐based solvents are extensively employed for CO₂capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by‐products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO₂. Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO₂affinity. Density functional theory (DFT) and symmetry‐adapted perturbation theory (SAPT) computations were performed for the calculation of CO₂interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO₂binding, as well as design principles that can further enhance CO₂capture through cooperative binding. 

Abstract We have performed a series of highly accurate calculations between CO₂and the 20 naturally occurring amino acids for the investigation of the attractive noncovalent interactions. Different nucleophilic groups present in the amino acid structures were considered (α‐NH₂, COOH, side groups), and the stronger binding sites were identified. A database of accurate reference interactions energies was compiled as computed by explicitly‐correlated coupled‐cluster singles‐and‐doubles, together with perturbative triples extrapolated to the complete‐basis‐set limit. The CCSD(F12)(T)/CBS reference values were used for comparing a variety of popular density functionals with different basis sets. Our results show that most density functionals with the triple‐zeta basis set def2‐TZVPP align with the CCSD(F12)(T)/CBS reference values, but errors range from 0.1 kcal/mol up to 1.0 kcal/mol. 

This primer helps the reader understand the basic categories of molecular representations and provides computational tools to generate molecular descriptors in each of these categories. After reading this primer, you will be able to use various methods to generate machine and/or human interpretable representations of molecular systems for inputs to machine learning models or for general chemical data science applications.