Multiscale modeling of hydrogen bonds and proton behavior in molecular and materials systems

Materials research is being transformed by the growing use of data-driven and computational approaches. This ongoing digital materials revolution requires integrating theoretical insights, computational modeling, and experimental studies. Computational simulations link fundamental theory with experimental observations, provide atomistic insight into structure, dynamics, and reactivity, and support the rational design of functional materials. This thesis employs multiscale modeling to investigate diverse systems, with a focus on hydrogen bonding interactions and proton behavior in condensed phases. Short hydrogen bonds are central to this work because they occur in many chemical and biological environments, including small molecules in both crystal and solution phases, as well as in proteins and protein–ligand complexes. A rare case is the formation of short hydrogen bonds in neutral liquids, such as neat mixtures of organic acids and bases. We therefore search for and predict such systems and examine their potential relevance in materials contexts. In addition, our broader interest lies in the idea that concepts well established in one field can lead to unexpected insights in another. This motivation supports our collaborations with experimental groups on general strategies for materials design, such as a conformational preorganization strategy used in polymer chemistry. In this thesis, I present my Ph.D. work, mostly carried out in close collaboration with experimental researchers, to uncover the physical mechanisms that govern these systems and to guide the rational design of new functional materials. By linking atomistic modeling with experimental observations, the results show that hydrogen bonds serve not merely as ubiquitous interactions but as structural and dynamical motifs that govern molecular organization in acid–base mixtures through extended hydrogen bond complexes, direct functional orientation and conformational preferences in polymers, and define the reactive architecture of protein active sites, which offer significant potential for the development of novel materials. This integrative approach illustrates how theory and computation complement experiment to advance predictive understanding and design of next-generation materials.