Kavli Affiliate: David T. Limmer
| First 5 Authors: Jorge L. Rosa-Raíces, Jorge L. Rosa-Raíces, , ,
| Summary:
We propose a method to compute free-energy differences from nonadiabatic
alchemical transformations using flow-based generative models. The method,
nonadiabatic force matching, hinges on estimating the dissipation along an
alchemical switching process in terms of a nonadiabatic force field that can be
learned through stochastic flow matching. The learned field can be used in
conjunction with short-time trajectory data to evaluate upper and lower bounds
on the alchemical free energy that variationally converge to the exact value if
the field is optimal. Applying the method to evaluate the alchemical free
energy of atomistic models shows that it can substantially reduce the
simulation cost of a free-energy estimate at negligible loss of accuracy when
compared with thermodynamic integration.
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