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Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
Published on: May 27, 2020
The Predictive Power of Chemical Bonding Analysis in Materials: A Perspective on Optoelectronic Properties
Gabriele Saleh1, Liberato Manna1
1Nanochemistry Department, Istituto Italiano di Tecnologia, via Morego 30, Genova 16163, Italy.
Abstract:
Chemical bonding governs how atoms interact to form compounds, thereby determining their physicochemical properties. Despite being an elusive concept, chemical bonding has led to the development of models and tools to explain and predict the behavior of chemical species. This perspective addresses the adoption of chemical bonding analysis in the study of optoelectronic materials, emphasizing the importance of its predictive aspect. After reviewing the evolution of chemical bonding models from the first Lewis formulation to the present day, the perspective discusses material classes and chemical bonding phenomena most relevant for light harvesting and emission. We delve into metal halide perovskites and structurally related materials, given their central role in optoelectronic research. Various aspects of chemical bonding in these materials are surveyed, from the structure-property relationship to the rationalization of their electronic properties through molecular orbital diagrams. Two chemical bonding features are particularly important for optoelectronic materials: the ns2 lone pairs of the cations typically found in these materials (e.g., Pb, Sb, and Bi) and the antibonding nature of valence and/or conduction bands. We discuss in depth the models to predict the implications of these two phenomena for optoelectronic properties. We also explore chalcohalides, a class of materials whose optoelectronic properties have recently emerged. From the chemical bonding perspective, these materials display intriguing phenomena due to the interplay of various types of chemical bonds. Finally, we discuss our vision on the role of chemical bonding analysis in the future of materials science, including synergies and antitheses with machine learning.
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