Local energetic coupling enhances the expressivity of chemical computation

2026-09-15

AI Summary

研究通过逆向设计热力学一致的化学反应网络，利用内能驱动增强化学计算的表现力，解决了控制化学系统计算表现力的物理化学特征问题。

Abstract

Living systems compute with chemistry by mapping environmental signals onto specific internal chemical states. Despite recent advances in molecular programming, it remains unclear which physicochemical features control the computational expressivity of chemical systems. Here we inverse-design thermodynamically consistent chemical reaction networks whose steady-state response to an environmental input computes a target nonlinear function. Using implicit differentiation we train the free-energy landscape directly: standard chemical potentials, transition-state energies and thermodynamic drives. Increasingly large networks generated by elementary ligation and cleavage steps fit increasingly complex nonmonotonic polynomial functions, with expressivity scaling logarithmically with network size, predicted primarily by the number of reactions. Training individual energetic parameter classes reveals that internal thermodynamic drives, capable of breaking detailed balance, dominate trainability, with comparable performances achieved only by pairs of parameter classes. These results identify nonequilibrium drive as the most effective single resource for steady-state computational expressivity in chemical reaction networks.

Problem

• chemical computation
• computational expressivity
• thermodynamic drives
• chemical reaction networks
• nonlinear function

Innovation

• thermodynamic drive
• chemical reaction networks
• nonlinear function
• steady-state response