Part III, Chapter 9

Transition State Theory

Eyring equation, activation energy, Arrhenius law, and potential energy surfaces

Historical Context

Transition state theory (TST) was independently developed by Henry Eyring and by Michael Polanyi and Meredith Evans in 1935. It provided the first theoretical framework for calculating absolute rate constants from molecular properties, bridging the gap between quantum mechanics, statistical mechanics, and chemical kinetics.

Svante Arrhenius had proposed his empirical equation $k = A e^{-E_a/RT}$ in 1889, but TST gave physical meaning to both the pre-exponential factor A and the activation energy $E_a$. The concept of a potential energy surface (PES), introduced by Fritz London and developed by Eyring and Polanyi, provided the topographical picture of chemical reactions that remains central to theoretical chemistry.

Key contributors: Svante Arrhenius (1889), Henry Eyring (1935), Michael Polanyi (1935), Meredith Evans (1935), Fritz London (1929).

Derivation 1: The Arrhenius Equation

Arrhenius observed that rate constants increase exponentially with temperature. He proposed:

Arrhenius Equation

$$k = A \exp\left(-\frac{E_a}{RT}\right)$$

where A is the pre-exponential factor and $E_a$ is the activation energy.

Taking logarithms:

$$\ln k = \ln A - \frac{E_a}{RT}$$

An Arrhenius plot of $\ln k$ vs $1/T$ yields a straight line with:

Slope

$-E_a/R$

Intercept

$\ln A$

The activation energy can also be defined operationally as:

$$E_a = RT^2 \frac{d\ln k}{dT}$$

Derivation 2: The Eyring Equation

TST assumes that reactants are in quasi-equilibrium with an activated complex (transition state) at the saddle point of the potential energy surface:

$$\text{A} + \text{B} \rightleftharpoons [\text{AB}]^\ddagger \to \text{products}$$

The equilibrium constant for forming the transition state:

$$K^\ddagger = \frac{[\text{AB}^\ddagger]}{[\text{A}][\text{B}]} = \exp\left(-\frac{\Delta G^\ddagger}{RT}\right)$$

The rate of crossing the barrier is determined by the frequency of the transition state decomposing along the reaction coordinate. Statistical mechanics gives this frequency as $k_BT/h$ (the classical vibration frequency of the loose mode at the saddle point):

Eyring Equation

$$k = \frac{k_B T}{h} \exp\left(-\frac{\Delta G^\ddagger}{RT}\right) = \frac{k_B T}{h} \exp\left(\frac{\Delta S^\ddagger}{R}\right) \exp\left(-\frac{\Delta H^\ddagger}{RT}\right)$$

At 298 K, $k_BT/h = 6.21 \times 10^{12} \text{ s}^{-1}$. This enormous frequency is modulated by the exponential factor: even at the maximum barrier-crossing rate, a barrier of 80 kJ/mol reduces the rate by a factor of $10^{14}$.

Derivation 3: Connecting Arrhenius and Eyring

The Arrhenius activation energy $E_a$ and the Eyring activation enthalpy$\Delta H^\ddagger$ are related but not identical. From the operational definition:

$$E_a = RT^2 \frac{d\ln k}{dT}$$

Differentiating the Eyring equation:

$$\frac{d\ln k}{dT} = \frac{1}{T} + \frac{\Delta H^\ddagger}{RT^2}$$
Unimolecular: $E_a = \Delta H^\ddagger + RT$
Bimolecular (gas): $E_a = \Delta H^\ddagger + 2RT$
Solution: $E_a = \Delta H^\ddagger + RT$

The pre-exponential factor is related to the activation entropy:

$$A = \frac{e \, k_B T}{h} \exp\left(\frac{\Delta S^\ddagger}{R}\right) \quad \text{(unimolecular)}$$

A large A (or positive $\Delta S^\ddagger$) indicates a loose transition state with more rotational and vibrational freedom. A small A (negative $\Delta S^\ddagger$) indicates a tight, ordered transition state.

Derivation 4: The Eyring Plot

To extract $\Delta H^\ddagger$ and $\Delta S^\ddagger$ separately, we use the Eyring plot. Rearranging the Eyring equation:

$$\ln\frac{k}{T} = -\frac{\Delta H^\ddagger}{R} \cdot \frac{1}{T} + \ln\frac{k_B}{h} + \frac{\Delta S^\ddagger}{R}$$

A plot of $\ln(k/T)$ vs $1/T$ gives:

Slope

$-\Delta H^\ddagger / R$

Intercept

$\ln(k_B/h) + \Delta S^\ddagger / R$

The Eyring plot separates the enthalpic and entropic contributions to the activation barrier. Since $\ln(k_B/h) = 23.76$, the intercept directly yields$\Delta S^\ddagger = R(\text{intercept} - 23.76)$.

Derivation 5: Potential Energy Surfaces

The potential energy surface (PES) is a multidimensional function$V(R_1, R_2, \ldots)$ that gives the electronic energy as a function of nuclear coordinates. For a simple A + BC → AB + C reaction with collinear geometry, the PES is a function of two distances: $r_{AB}$ and $r_{BC}$.

Key Features of the PES

Reactant valley: Minimum along $r_{BC}$ at large $r_{AB}$

Product valley: Minimum along $r_{AB}$ at large $r_{BC}$

Saddle point: The transition state - a maximum along the reaction coordinate but a minimum in the perpendicular direction

Minimum energy path (MEP): The path of steepest descent from the saddle point to both valleys

The Born-Oppenheimer approximation allows the PES to be computed by solving the electronic Schrodinger equation at fixed nuclear positions. Modern computational chemistry can calculate PES points with high accuracy using methods such as:

DFT

Density functional theory for large systems

CCSD(T)

Gold standard for small molecules

CASPT2

For multireference systems

The activation energy in TST corresponds to the energy difference between the saddle point and the reactant minimum, corrected for zero-point vibrational energy:$\Delta E^\ddagger_0 = E_{\text{saddle}} - E_{\text{react}} + \Delta\text{ZPE}$.

Applications

Drug Design

Enzyme inhibitors are designed to resemble the transition state of the catalyzed reaction. Transition state analogs bind more tightly than substrates because enzymes preferentially stabilize the transition state (Pauling's principle, 1946).

Catalysis

Catalysts work by lowering $\Delta G^\ddagger$. Heterogeneous catalysts provide alternative PES pathways with lower saddle points. Sabatier's principle states that the optimal catalyst binds intermediates neither too weakly nor too strongly.

Computational Chemistry

Modern quantum chemistry calculates transition state structures and activation barriers to predict rate constants. Programs like Gaussian, ORCA, and VASP enable routine barrier calculations for reactions of industrial and biological importance.

Kinetic Isotope Effects

Replacing H with D changes the zero-point energy and thus the effective barrier height. Primary KIEs ($k_H/k_D \approx 2-7$) indicate bond-breaking at the transition state, providing mechanistic evidence.

Simulation: Eyring, Arrhenius, and PES

This simulation visualizes a one-dimensional potential energy surface, generates Arrhenius plots for different activation energies, computes Eyring equation rate constants, and demonstrates how the Eyring plot separates enthalpic and entropic contributions.

Transition State Theory: PES, Arrhenius, and Eyring

Python
script.py168 lines

Click Run to execute the Python code

Code will be executed with Python 3 on the server

Collision Theory and Its Limitations

Before TST, collision theory provided the first microscopic picture of reaction rates. From the kinetic theory of gases, the collision rate between A and B is:

$$Z_{AB} = n_A n_B \sigma_{AB} \left(\frac{8k_BT}{\pi\mu}\right)^{1/2}$$

where $\sigma_{AB} = \pi(r_A + r_B)^2$ is the collision cross-section and$\mu$ is the reduced mass. The rate constant becomes:

$$k = p \cdot \sigma_{AB} \left(\frac{8k_BT}{\pi\mu}\right)^{1/2} \exp\left(-\frac{E_a}{RT}\right)$$

where p is the steric factor ($0 < p \leq 1$)

Collision theory explains the Arrhenius equation but has significant limitations:

Problem 1: The steric factor p is not predicted -- it must be fitted. For complex molecules, p can be as small as $10^{-9}$.
Problem 2: It treats molecules as hard spheres with no internal structure. TST overcomes this by using the full partition function of the transition state.
Problem 3: It cannot handle unimolecular reactions (no collision partner). TST and RRKM theory address this naturally.

Beyond TST: RRKM Theory

Rice-Ramsperger-Kassel-Marcus (RRKM) theory extends TST to energy-dependent unimolecular reactions. It gives the microcanonical rate constant:

$$k(E) = \frac{N^\ddagger(E - E_0)}{h \, \rho(E)}$$

where $N^\ddagger(E - E_0)$ is the sum of states at the transition state and$\rho(E)$ is the density of states of the reactant.

RRKM theory correctly predicts:

• The Lindemann falloff curve and its detailed shape
• Product energy distributions in unimolecular decomposition
• The pressure and temperature dependence of thermal unimolecular rate constants

When thermally averaged (integrated over a Boltzmann distribution of energies), RRKM reduces to canonical TST in the high-pressure limit, confirming the consistency of the two theories.

Quantum Tunneling and Corrections to TST

Classical TST assumes that the system must have enough energy to surmount the barrier. Quantum mechanics allows particles to tunnel through the barrier even when their energy is below $\Delta E^\ddagger$. This is particularly important for light particles (H atoms, protons, electrons).

$$k_{\text{quantum}} = \kappa \cdot k_{\text{TST}}$$

where $\kappa$ is the transmission coefficient. The simplest tunneling correction is the Wigner correction:

Wigner Tunneling Correction

$$\kappa_{\text{Wigner}} = 1 + \frac{1}{24}\left(\frac{h\nu^\ddagger}{k_BT}\right)^2$$

where $\nu^\ddagger$ is the imaginary frequency at the saddle point.

Evidence for tunneling includes:

Anomalously large KIEs: Primary kinetic isotope effects $k_H/k_D > 7$exceed the classical limit, indicating significant H-tunneling.
Curved Arrhenius plots: At low temperatures, the Arrhenius plot curves upward because tunneling provides an additional pathway below the classical barrier.
Temperature-independent rates: At very low temperatures (cryogenic), some reactions proceed at rates independent of temperature -- pure tunneling.

Marcus Theory of Electron Transfer

Rudolph Marcus (Nobel Prize 1992) extended TST to electron transfer reactions, providing a remarkable relationship between the rate constant, the driving force ($\Delta G^\circ$), and the reorganization energy ($\lambda$):

Marcus Equation

$$\Delta G^\ddagger = \frac{(\lambda + \Delta G^\circ)^2}{4\lambda}$$

This predicts the remarkable "Marcus inverted region": when$-\Delta G^\circ > \lambda$, the reaction actually slows down as it becomes more thermodynamically favorable. This counterintuitive prediction was experimentally confirmed and has profound implications for photosynthesis, photovoltaics, and molecular electronics.

Normal Region

$-\Delta G^\circ < \lambda$

Rate increases with driving force

Optimal

$-\Delta G^\circ = \lambda$

Barrierless, maximum rate

Inverted Region

$-\Delta G^\circ > \lambda$

Rate decreases with driving force

Chapter Summary

Arrhenius: $k = A\exp(-E_a/RT)$; empirical; slope of ln(k) vs 1/T gives $-E_a/R$.

Eyring: $k = (k_BT/h)\exp(-\Delta G^\ddagger/RT)$; theoretical; connects rate constants to thermodynamic activation parameters.

Connection: $E_a = \Delta H^\ddagger + nRT$ where n = 1 (unimolecular) or 2 (bimolecular gas).

Eyring plot: ln(k/T) vs 1/T separates $\Delta H^\ddagger$ (slope) from $\Delta S^\ddagger$ (intercept).

PES: The reaction coordinate connects reactant and product valleys through a saddle-point transition state.

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