A mathematical framework for thermodynamic computing with applications to chemical reaction networks

Abstract The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

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Publication Details

Journal
npj Unconventional Computing
Published
2026-04-01
DOI
https://doi.org/10.1038/s44335-026-00057-5
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

Antonino Tumeo, Nícolas Bohm Agostini, William R. Cannon, Connah Johnson
npj Unconventional Computing
Neural Networks and Reservoir Computing
article

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

Antonino Tumeo, Nícolas Bohm Agostini, William R. Cannon, Connah Johnson
article en

Abstract

Abstract The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

npj Unconventional ComputingVol. 3(1)
Pacific Northwest National Laboratory (US)
U.S. Department of Energy, Battelle, Advanced Scientific Computing Research, Pacific Northwest National Laboratory
Affordable and clean energy
Openalex Percentile: Top 38%
Neural Networks and Reservoir Computing
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A mathematical framework for thermodynamic computing with applications to chemical reaction networks — Antonino Tumeo, Nícolas Bohm Agostini, et al. · npj Unconventional Computing (2026) | TGRS Research Map | TGRS