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Kinetic frustration enables single-molecule computation.

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This study shows how single molecules can compute using thermal-kinetic frustration, enabling deterministic pattern recognition in noisy environments. This opens new avenues for microscopic information processing and adaptive materials.

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Area of Science:

  • Non-equilibrium statistical mechanics
  • Molecular computing
  • Physical systems

Background:

  • Implementing computation at the microscopic scale is challenging due to thermal fluctuations.
  • Biological systems use complex molecular networks for computation.
  • Physical principles for simple systems processing temporal information are not well understood.

Purpose of the Study:

  • To demonstrate how non-equilibrium dynamics enable single molecules to perform computation.
  • To explore the principle of thermal-kinetic frustration for information processing.
  • To create a physical realization of a deterministic finite automaton using a linear polymer.

Main Methods:

  • Engineering thermal-kinetic frustration in a linear polymer with N binary-state units.
  • Utilizing non-equilibrium driving to access 2N configurations.
  • Employing mechanical control signals for pattern recognition.

Main Results:

  • A single molecule was engineered to act as a deterministic finite automaton.
  • The system accessed 2N configurations via non-equilibrium driving, exceeding equilibrium limits (N+1).
  • Deterministic evolution of the molecule's dominant configuration was observed despite thermal noise.

Conclusions:

  • Stochastic microscopic dynamics can lead to deterministic computation.
  • The framework provides insights into non-equilibrium statistical mechanics and physical information processing.
  • Theoretical predictions are testable with DNA nanotechnology, suggesting applications in biosensing and adaptive materials.