Several types of algorithms are commonly employed in catalysis research:
1. Machine Learning Algorithms: These include neural networks, decision trees, and support vector machines, which can predict catalyst performance based on historical data. 2. Genetic Algorithms: Inspired by natural selection, these algorithms optimize catalysts by iteratively selecting and combining the best-performing candidates. 3. Quantum Mechanical Calculations: Methods like Density Functional Theory (DFT) are used to understand the electronic structure of catalysts at a molecular level. 4. Molecular Dynamics Simulations: These simulations provide insights into the behavior of catalysts under different conditions, helping to predict stability and reactivity.
