Computational simulations of entropy are important in understanding the thermodynamic forces that drive chemical reactions on a molecular scale. In recent years, various algorithms have been developed and applied in conjunction with molecular modeling techniques to evaluate the change of entropy in solvation, hydrophobic interactions, and chemical reactions. The aim of this review is to highlight four specific computational entropy calculation methods: normal mode analysis, free volume theory, two‐phase thermodynamics, and configurational entropy modeling. The technical aspects, applications, and limitations of each method will be discussed in detail.

Reaction dynamics trajectory simulations have been conducted to predict the product ratio of reactions with post-transition state bifurcation. However, it remains unknown how the entropy of reactive species along the reaction path mediates ambimodal selectivity. Here, by leveraging deep generative model, we developed an accelerated entropic path sampling approach that evaluates the change of entropy along the post-transition-state reaction path for each product using merely a few hundred reaction dynamic trajectories. The new method, called bidirectional generative adversarial network - entropic path sampling (BGAN-EPS), can enhance the estimation of probability density functions of molecular configurations by generating pseudo-molecular configurations that are statistically indistinguishable from the true data. The method was tested using cyclopentadiene dimerization as a model reaction, in which we reproduced the reference entropic profiles (derived from 2,480 trajectories) using merely 124 trajectories. We further applied BGAN-EPS method to NgnD-catalyzed Diels–Alder reaction to investigate the entropic origin behind its ambimodal selectivity. The results show that the ambimodal preference towards the formation of the [6+4]-adduct over the [4+2]-adduct is contributed by both energetic and entropic forces.

Computational simulations of entropy are important in understanding the thermodynamic forces that drive chemical reactions on a molecular scale. In recent years, various algorithms have been developed and applied in conjunction with molecular modeling techniques to evaluate the change of entropy in solvation, hydrophobic interactions, and chemical reactions. The aim of this review is to highlight four specific computational entropy calculation methods: normal mode analysis, free volume model, two-phase thermodynamics, and configurational entropy modeling. The technical aspects, applications, and limitations of each method will be discussed in detail.

The role of entropy in mediating the dynamic outcomes of chemical reactions remains largely unknown. To evaluate the change of entropy along post-transition state paths, we have previously developed entropic path sampling that computes configurational entropy from an ensemble of reaction trajectories. However, one major caveat of this approach lies in its high computational demand: about 2000 trajectories are needed to converge the computation of an entropic profile. Here, by leveraging a deep generative model, we developed an accelerated entropic path sampling approach that evaluates entropic profiles using merely a few hundred reaction dynamic trajectories. The new method, called bidirectional generative adversarial network−entropic path sampling, can enhance the estimation of probability density functions of molecular configurations by generating pseudo-molecular configurations that are statistically indistinguishable from the true data. The method was established using cyclopentadiene dimerization, in which we reproduced the reference entropic profiles (derived from 2480 trajectories) using merely 124 trajectories. The method was further benchmarked using three reactions with symmetric post-transition-state bifurcation, including endo-butadiene dimerization, 5-fluoro-1,3cyclopentadiene dimerization, and 5-methyl-1,3-cyclopentadiene dimerization. The results indicate the existence of a "hidden entropic intermediate", which is a dynamic species that binds to a local entropic maximum where no free energy minimum is formed.