In a previous blog, we looked at how BIOVIA Materials Studio can aid in computing reaction pathways, activation energies, transition states, and reaction rates for individual steps in chemical reactions. Materials Studio’s new Swiss army knife in this respect is FlexTS, which revolutionizes transition state searching. In this blog, we will build on the chemical processes and rates discussed earlier and talk about how to understand chemical dynamics using Materials Studio.
Chemical Reaction Dynamics in BIOVIA Materials Studio
BIOVIA Materials Studio contains multiple tools to study reaction dynamics at different levels of detail, all based on chemical process rates.
Materials Studio Cantera can integrate reaction chemistries for very large systems, such as combustion or catalytic reactors. It uses reaction mechanisms for calculating species evolution in the entire reactor and can cover length and time scales larger than almost anything else in Materials Studio – centimeters and minutes or even hours. Materials Studio’s advantage is the ability to compute reaction rates using FlexTS and the ReactionKinetics tasks described in the previous post and seamlessly integrate these into existing reaction mechanisms.
Materials Studio Kinetix is an implementation of the kinetic Monte Carlo method for studying reactive surfaces in atomistic detail. It provides a simple interface for defining processes on surfaces, again based on the rates and reaction energies computed using the reaction analysis tools in Materials Studio’s quantum mechanics portfolio. Kinetix allows the detailed design of catalytic surfaces and optimal reaction conditions based on these individual rates.
The primary focus of this post is to introduce ReactionFinder, available since the Materials Studio 2021 release. This enables you to study catalysis in realistic liquid environments and provides atomic-level insights into complex structural relationships. ReactionFinder combines the reaction rate analysis, highlighted in a previous blog post, with traditional molecular dynamics and kinetic Monte Carlo simulations in one integrated method.
A typical ReactionFinder project begins by preparing a reaction mechanism using the output from reaction rate analysis, for example the structures for reactants and products. The setup is intuitive and graphical in the Materials Studio Visualizer. You can then apply this overall mechanism to molecular dynamics simulations by regularly checking which reactions are possible, then selecting and executing an individual reaction. By repeating this process many times over, you can study the chemical evolution of molecular systems in unprecedented detail. Since the reaction probabilities in ReactionFinder are founded on quantum-mechanical reaction rate calculations while running at the speed of classical molecular dynamics. ReactionFinder can also detect electron transfer reactions.
A key feature of applying ReactionFinder is that it chooses its molecular dynamics simulation times according to the total reaction rate, in a similar manner to kinetic Monte Carlo simulations. This selection is the key to ensuring that the overall reaction dynamics is represented accurately and that you choose the relative time scales for reactions and diffusion accordingly. Overall, this technique combines the best of quantum mechanical DFT, molecular dynamics, and kinetic Monte Carlo simulations to study chemical evolutions.
Simulating the Electrode/Electrolyte Interface in Lithium Ion Batteries
You can study many possible reactive liquids using ReactionFinder. As a proof of concept, we used it to model the degradation of an electrolyte in Lithium Ion batteries near the anode. This process begins during the charging phase of a battery when electrons emitted from the anode do not connect with Li ions in solution, but instead break up one of the electrolyte molecules in a reduction reaction. After this initial step, an entire cascade of reaction leads to a build-up of degradation products near the anode into a layer called the Solid Electrolyte Interphase (SEI).
ReactionFinder allowed us to study the formation and atomic-level structure of the SEI, starting from a reaction mechanism computed using FlexTS as discussed in the previous post.
Read the paper: https://pubs.acs.org/doi/full/10.1021/acs.jctc.1c00921