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Arrhenius Plots

Arrhenius Plots

The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...

Hess's Law

Hess's Law

There are two ways to determine the amount of heat involved in a chemical change: measure it experimentally, or calculate it from other experimentally determined enthalpy changes. Some reactions are difficult, if not impossible, to investigate and make accurate measurements for experimentally. And even when a reaction is not hard to perform or measure, it is convenient to be able to determine the heat involved in a reaction without having to perform an experiment.

Catalysis

Catalysis

The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.

The Born-Haber Cycle

The Born-Haber Cycle

Enthalpy

Enthalpy

Chemists ordinarily use a property known as enthalpy (H) to describe the thermodynamics of chemical and physical processes. Enthalpy is defined as the sum of a system’s internal energy (E) and the mathematical product of its pressure (P) and volume (V):

Enthalpies of Reaction

Enthalpies of Reaction

Hess’s law can be used to determine the enthalpy change of any reaction if the corresponding enthalpies of formation of the reactants and products are available. The main reaction may be divided into stepwise reactions : (i) decompositions of the reactants into their component elements, for which the enthalpy changes are proportional to the negative of the enthalpies of formation of the reactants, −ΔHf°(reactants), followed by (ii) re-combinations of the elements (obtained...

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Updated: May 25, 2025

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

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Accessing Numerical Energy Hessians with Graph Neural Network Potentials and Their Application in Heterogeneous

Brook Wander¹, Joseph Musielewicz¹, Raffaele Cheula^1,2

¹Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

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Summary

Machine learning potentials accurately calculate the Hessian for surface intermediates, enabling Gibbs free energy calculations. This approach improves transition state searches and accounts for adsorbate translational entropy.

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Area of Science:

Computational chemistry
Materials science
Catalysis

Background:

Access to the potential energy Hessian is crucial for calculating Gibbs free energy and optimizing transition states.
Machine learning potentials (MLPs) offer a promising avenue for accelerating these calculations.

Purpose of the Study:

To evaluate the accuracy of pretrained Open Catalyst Project (OCP) MLPs in determining the Hessian for adsorbed intermediates.
To assess the utility of MLP-derived Hessians for Gibbs free energy calculations and transition state searches.
To investigate the contribution of adsorbate translational entropy beyond the harmonic approximation.

Main Methods:

Utilized off-the-shelf pretrained OCP MLPs to compute the potential energy Hessian for surface-adsorbed intermediates.
Calculated vibrational entropy contributions to Gibbs free energy using MLP-derived Hessians.
Explored the impact of adsorbate translational entropy at 300 K.
Applied MLP-determined Hessian information to transition state search algorithms.

Main Results:

OCP MLPs accurately determine the Hessian for adsorbed intermediates with a mean absolute error (MAE) of 58 cm^-1.
The top-performing MLP model, with an offset correction, estimated vibrational entropy with an MAE of 0.042 eV at 300 K.
94% of randomly sampled systems exhibited translational entropy greater than 0.1 eV at 300 K.
MLP-Hessian information reduced unconverged transition states by 65-93% in searches.

Conclusions:

Pretrained OCP MLPs can reliably determine the Hessian for surface intermediates, enabling accurate Gibbs free energy calculations.
MLPs provide a valuable tool for incorporating vibrational and translational entropy, crucial for understanding adsorption phenomena.
The use of MLP-derived Hessians significantly enhances the efficiency and convergence of transition state searches in catalysis.