Single-molecule Automata: Harnessing Kinetic-Thermodynamic Discrepancy for Temporal Pattern Recognition

TL;DR

This paper introduces a theoretical framework for single-molecule computers that utilize kinetic-thermodynamic discrepancies to perform temporal pattern recognition, enabling complex information processing at the molecular scale.

Contribution

It presents a novel design for a molecular automaton using energy seascapes and non-equilibrium protocols to achieve deterministic state control and pattern recognition.

Findings

Demonstrates a single polymer can act as a finite automaton

Shows deterministic evolution of molecular states in response to signals

Proposes feasible experimental implementations with DNA nanotechnology

Abstract

Molecular-scale computation is crucial for smart materials and nanoscale devices, yet creating single-molecule systems capable of complex computations remains challenging. We present a theoretical framework for a single-molecule computer that performs temporal pattern recognition and complex information processing. Our approach introduces the concept of an energy seascape, extending traditional energy landscapes by incorporating control parameter degrees of freedom. By engineering a kinetic-thermodynamic discrepancy in folding dynamics, we demonstrate that a linear polymer with $N$ binary-state foldable units can function as a deterministic finite automaton, processing $2^N$ configurations. The molecule's dominant configuration evolves deterministically in response to mechanical signals, enabling recognition of complex temporal patterns. This design allows complete state controllability through non-equilibrium driving protocols. Our model opens avenues for molecular-scale computation with applications in biosensing, smart drug delivery, and adaptive materials. We discuss potential experimental realizations using DNA nanotechnology. This work bridges the gap between information processing devices and stochastic molecular systems, paving the way for sophisticated molecular computers rivaling biological systems in complexity and adaptability.