1. Linear Scaling Methods: These methods reduce the computational effort from a cubic scaling (O(N^3)) to linear scaling (O(N)), where N is the number of atoms. Techniques such as ONIOM (Our own N-layered Integrated molecular Orbital and Molecular mechanics) and QM/MM (Quantum Mechanics/Molecular Mechanics) fall under this category.
2. Fragment-Based Methods: These involve breaking down a large system into smaller, more manageable fragments. Divide-and-Conquer (DC) and Fragment Molecular Orbital (FMO) methods are examples. By solving smaller fragments separately and then combining the solutions, computational resources are conserved.
3. Machine Learning (ML) Approaches: ML algorithms can be trained to predict the properties of catalytic systems based on a database of known results. Techniques like Neural Networks and Kernel Ridge Regression can significantly speed up the prediction of catalytic properties.
