It's about time: a thermodynamic information criterion (TIC)

TL;DR

This paper introduces the thermodynamic information criterion (TIC), a new scalar measure inspired by AIC, to evaluate chemical processes based on their efficiency in reaching a desired steady state with minimal time and energy loss.

Contribution

The paper proposes TIC as a novel thermodynamic criterion that extends statistical optimization principles into chemical physics for process evaluation.

Findings

TIC provides a quantitative measure to compare chemical processes.

TIC correlates with energy and time efficiency in reaching steady states.

The approach bridges thermodynamics and machine learning for process optimization.

Abstract

Useful chemical processes often involve a desired steady state probability distribution, equilibrium or not, from which product is extracted. Given many different ways to attain the same steady state, which candidate "loses" the least in terms of time and energy? A scalar thermodynamic information criterion (TIC), inspired by AIC, assigns lower values to chemical processes with less estimated "loss" to generate the same desired steady state. As an element of thermodynamic machine learning, TIC naturally extends statistical objective optimization into the realm of chemical physics.