Universal and Efficient Entropy Estimation Using a Compression Algorithm

Characterization of simulated physical systems in equilibrium typically requires calculation of free-energy as a function of some control parameter. The enthalpy of a system is calculable using the apriori choice of interactions (i.e., force field, coupling parameters), yet entropy remains a challenge to quantify. Current free-energy and entropy estimation techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. In this talk, I will present a new universal framework to calculate entropy using lossless compression algorithms readily available in every computer [1]. Furthermore, I will demonstrate the scheme’s effectiveness in several model systems and discuss convergence due to various data representations and sampling. The presented scheme is demonstrated to be a practical alternative for entropy calculation in simulated systems, regardless of model specific details, and may also be applied to experimentally recorded data.

[1] R. Avinery, M. Kornreich, R. Beck (2017) arXiv:1709.10164