Exact chemo--thermal Metropolis Brownian engine: chemical leverage, temperature-neutral stall, power optimization, and multicyclic dissipation

What the math gives to ML

The paper provides an exactly solvable nonequilibrium cycle whose transferable mechanism is the separation between cycle affinity, net current, stall, and hidden dissipation. Its key quantitative object is an additive affinity combining thermal differences, chemical driving, and mechanical load; zero net current occurs at a computable stall condition, while multicyclic extensions can retain positive entropy production at zero observable motion through futile cycles. A neural-network optimizer can use this structure as a three-phase cyclic update with separately controlled noise levels and an auxiliary drive, while monitoring affinity and current rather than treating zero parameter displacement as evidence of equilibrium. The most useful tests are a sharp current reversal at predicted stall and saturation of current when only one phase bottleneck is accelerated.