Enhanced Sampling Techniques: Methods such as metadynamics, accelerated MD, and replica exchange MD help overcome the time scale limitations by improving the sampling of rare events and long timescale processes. Machine Learning: The integration of machine learning with MD has led to the development of more accurate force fields and the ability to predict complex behaviors in catalytic systems more efficiently. Multi-scale Modeling: Combining MD with other computational techniques, such as quantum mechanics and continuum models, allows for a more comprehensive understanding of catalytic processes across different scales.
