Transition path time distribution and the transition path free energy barrier
Literature Information
The recent experimental measurement of the transition path time distributions of proteins presents several challenges to theory. Firstly, why do the fits of the experimental data to a theoretical expression lead to barrier heights which are much lower than the free energies of activation of the observed transitions? Secondly, there is the theoretical question of determining the transition path time distribution, without invoking the Smoluchowski limit. In this paper, we derive an exact expression for a transition path time distribution which is valid for arbitrary memory friction using the normal mode transformation which underlies Kramers' rate theory. We then recall that for low barriers, there is a noticeable difference between the transition path time distribution obtained with absorbing boundary conditions and free boundary conditions. For the former, the transition times are shorter, since recrossings of the boundaries are disallowed. As a result, if one uses the distribution based on absorbing boundary conditions to fit the experimental data, one will find that the transition path barrier will be larger than the values found based on a theory with free boundary conditions. We then introduce the paradigm of a transition path barrier height, and show that one should always expect it to be much smaller than the activation energy.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.












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