Cassandra operates by integrating various computational methods to provide a comprehensive understanding of catalytic reactions. These methods include:
- Quantum Mechanical Models: These models provide detailed insights into the electronic structure of catalysts, helping predict their reactivity and selectivity. - Molecular Dynamics Simulations: These simulations explore the behavior of catalysts at the atomic level over time, offering insights into their stability and performance. - Machine Learning Algorithms: These algorithms analyze vast datasets to identify patterns and predict the outcomes of catalytic reactions. - High-throughput Screening: This technique rapidly tests a large number of catalyst candidates, narrowing down the most promising options for further study.
