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# System prepended metadata

title: 'Activation Energy and Chemical Compounding in AMMs: A Mathematical Perspective'

---

# Activation Energy and Chemical Compounding in AMMs: A Mathematical Perspective

## Abstract
This paper explores the analogy between activation energy in autocatalytic reactions and the behavior of smart contracts in automated market maker (AMM) pools. Drawing upon classical chemical principles and applying them to the decentralized finance (DeFi) landscape, we provide a unique perspective on the dynamics of cryptocurrency balances held by AMMs.

## 1. Introduction

Chemical reactions often require an initial energy input to proceed, known as activation energy. In the world of DeFi, a parallel can be drawn with smart contracts, where certain triggers or 'energy' is needed to rebalance liquidity pools or issue new state information.

## 2. Modeling Smart Contract Dynamics

### 2.1 Trigger Waves

Inside smart contracts, trigger waves are analogous to excited reaction-diffusion wavefronts that propagate through a medium and annihilate upon contact. Represented mathematically as:

\[ F1(x,y) = K1x \times K1y \]

Where:
- \( F1 \) is the waveform of the pulse in the first medium.
- \( K1x, K1y \) are components of the wave vector denoting direction and magnitude.

In AMMs, these waves can model the dynamics of liquidity pools, reflecting imbalances arising from varying demand or supply.

### 2.2 Pseudowaves

Distinguished from trigger waves, pseudowaves are stable, non-propagating waves formed by the interaction of trigger waves with reaction vessel boundaries. Their stability can model equilibrium states in liquidity pools.

## 3. Belousov-Zhabotinsky (BZ) Reaction Analogy

The BZ reaction, characterized by oscillating behavior, mirrors the fluctuations witnessed in cryptocurrency balances in AMMs. Representing this analogy:

\[ BZ(t) = f(x(t), y(t)) \]

Where:
- \( BZ(t) \) is the oscillating behavior at time \( t \).
- \( x(t) \) and \( y(t) \) are units of cryptocurrencies x and y respectively at time \( t \).

## 4. AMM in a 3D Computational Grid

Visualizing the AMM in a 3D grid, cryptocurrency balances are represented as points interacting dynamically. However, these balances merely reflect states; computational processes are external to the smart contract.

\[ AMM(x,y) = \begin{bmatrix} x \\ y \end{bmatrix} \]

Where:
- \( AMM(x,y) \) denotes the state of cryptocurrency balances.

## 5. Wave Interference in AMMs

### 5.1 Wave Refraction and Filtering

Applying the Snell–Descartes law to AMMs, we understand the angle of incidence and refraction of waves:

\[ n_1 \times \sin(\theta_1) = n_2 \times \sin(\theta_2) \]

Where:
- \( n_1, n_2 \) are refractive indices of the two media.
- \( \theta_1 \) is the angle of incidence and \( \theta_2 \) is the angle of refraction.

In DeFi, the smart contract serves as a unique medium, filtering waves. Incidence and refraction dynamics highlight the difference in behaviors at and within the smart contract's boundaries.

### 5.2 Intersecting Wavefronts

The behavior of a system's components is critical to understanding the system itself. In our context, intersecting wavefronts in AMMs can provide insights into potential imbalances and the system's overall behavior.

## Conclusion

Through the lens of chemical principles, the dynamics of AMMs can be better understood, offering insights and potential strategies for optimized performance in the decentralized finance landscape. As DeFi evolves, these models may offer enhanced predictability and stability to the ecosystem.