@inbook{a511fe1387da4181bc2169490b8b76a8,
title = "Assessing entropy for catalytic processes at complex reactive interfaces",
abstract = "When chemical reactions are accelerated by a catalyst, entropy differences between reactants and their transient intermediates can be the driving force behind the promotion or inhibition of desired and parasitic chemical pathways. Understanding and controlling catalytic processes therefore requires both a fundamental and practicable understanding of entropy in addition to enthalpy. In unstructured media such as the vapor phase equilibrated with sparsely covered surfaces, entropy can be adequately accounted for by well-established approaches based on translational, rotational, and harmonic vibrational partition functions. However, these approximations become inadequate in more complex condensed phase environments, e.g., solid-liquid interfaces of confined reaction spaces. In this chapter, we provide an overview of the state-of-art in the computational quantification of entropy and its known ramifications on catalysis. The fundamental roles of thermodynamics and kinetics in catalysis are covered in enough detail to appreciate and contextualize the computational methods employed to compute chemically accurate estimates of entropy. These methods are discussed in appropriate detail and range from the ubiquitous harmonic oscillator approximation where entropy unrelated to high frequency oscillations is typically underestimated, to enhanced free energy sampling with molecular dynamics where the desired accuracy must be weighed against the associated computational cost of obtaining it. The rising importance of machine learning and artificial intelligence in accelerating methodological progress in this field is touched upon, as well. Finally, applications, successes, and pitfalls of using these methods are provided to showcase past and present accomplishments while clarifying where improvements in both understanding and methodology are still needed.",
keywords = "Catalysis, Data science, Enhanced sampling, Entropy, Molecular simulations, Reaction pathways",
author = "Loukas Kollias and Gregory Collinge and Difan Zhang and Allec, {Sarah I.} and Gurunathan, {Pradeep Kumar} and Piccini, {Giovanni Maria} and Yuk, {Simuck F.} and Nguyen, {Manh Thuong} and Lee, {Mal Soon} and Glezakou, {Vassiliki Alexandra} and Roger Rousseau",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
month = jan,
doi = "10.1016/bs.arcc.2022.09.004",
language = "English",
isbn = "9780323990929",
series = "Annual Reports in Computational Chemistry",
publisher = "Elsevier Ltd",
pages = "3--51",
editor = "Dixon, {David A.}",
booktitle = "Annual Reports in Computational Chemistry",
}