Machine Learning, Deep Learning, Chemistry, Physics, Molecular Dynamics.
trajectory, so we can expect that they are typical and we are likely to run into similar (but bigger) configurations once we use the model. So it’s very beneficial if the model performs well on these configurations. The downside however is that, since the timestep taken in aiMD has to be very