ML-dip.py takes as input cartesian coordinates (XYZ-traj1.xyz) and dipole moments (DIP-traj1.dat) from a trajectory, and outputs dipole moments corresponding to another trajectory (XYZ-traj2.xyz).
Abstract: Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without in-ductive bias, MLPs perform well on feature extraction and achieve amazing results.
Abstract: Artificial neural networks (NNs) have become the de facto standard in machine learning. They allow learning highly nonlinear transformations in a plethora of applications. However, NNs ...
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