Function, Complexity and Thermodynamics in Adaptive and Intelligent Soft Matter Systems: An Information-Theoretical Framework

Abstract

The terms responsive, adaptive and intelligent are widely used in soft matter and materials science but remain qualitative, with no quantitative basis for comparing systems from different areas on a common axis. We address this by treating any stimulus-coupled material as an information channel. The three classes are distinguished by kernel conditioning: a memoryless map p(y|x) (responsive), a state-conditioned map p(y|x,s) (adaptive), and a feedback-modified channel with memory (intelligent). Three information-theoretic metrics follow: configurational diversity I1, functional selectivity I2, and stimulus-response information transfer I3. Because the kernel and substrate are the same object, a heuristic non-monotonic relationship arises between internal complexity and realised information transfer, with an optimal complexity N* set by transmission efficiency, stimulus energy and thermal noise. We propose two benchmarking planes: a dynamic plane (volumetric information rate I3/V versus power density P, referenced to the Landauer-Berut floor) and a static plane (I1, I2). Sixteen systems spanning synthetic soft matter, biology and hard matter separate into broad bands above the benchmark: ~10^18-10^20x for soft matter and shape-memory alloys, 10^10-10^16x for silicon and electromechanical devices, 10^9-10^10x for neuromorphic memristors, and 10^5-10^8x for evolved biology, with an ER lipid network near 10^11x. Each placement carries an uncertainty of one to two decades per axis; the band ordering nonetheless appears robust. The gap between synthetic soft matter and biology is tentatively attributed to the per-element substrate energy scale (1-10 kBT versus 10^4-10^5 kBT). The framework is offered as a foundation that may lead to validated design rules; three routes are proposed by which soft matter's separation from the Landauer benchmark might be reduced.