From Free-Energy Profiles to Activation Free Energies
Johannes C. B. Dietschreit (1), Dennis J. Diestler (2), Andreas Hulm, (3), Christian Ochsenfeld (3, 4), Rafael G\'omez-Bombarelli (1) ((1), Department of Materials Science, Engineering, Massachusetts Institute of, Technology, Cambridge, Massachusetts, USA, (2) University of

TL;DR
This paper critically examines the use of free-energy profiles (FEPs) for estimating activation free energies in chemical reactions, deriving an exact expression that clarifies the limitations and dependencies on the choice of collective variables.
Contribution
The authors derive an exact formula for activation free energies that removes ambiguities caused by the choice of collective variables, improving accuracy over traditional FEP-based methods.
Findings
FEP-based approximation is valid only at low temperatures and with small effective mass CVs.
The activation free energy depends on reactant probability, transition state density, and thermal wavelength.
Choice of CV significantly affects estimated activation free energies, but not the overall free energy change.
Abstract
Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) that discriminates between R and P, one can define a free-energy profile (FEP) as the logarithm of the marginal Boltzmann distribution of the CV. The FEP is not a true free energy, however, it is common to treat the FEP as the free-energy analog of the minimum energy path on the PES and to take the activation free energy, , as the difference between the maximum of the FEP at the transition state and the minimum at R. We show that this approximation can result in large errors. Since the FEP depends on the CV, it is therefore not unique, and different, discriminating CVs can yield different activation free energies for the same reaction. We derive an exact expression for the activation free energy that avoids this ambiguity…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
