Machine learning algorithms for atomistic systems have the potential to circumvent expensive quantum mechanical calculations and enable computations for large systems which are conventionally infeasible. In this way, the complexity of solving the many-body Schr odinger equation is reduced by mapping to statistical models. The appropriate data representation is crucial in increasing the accuracy, e ciency and reliability of the model. In this thesis, we conduct an in-depth evaluation of handcrafted and neural network learned representations for molecules, inorganic crystals and adsorbate-surface systems. In addition to evaluating the atomistic machine learning models by the mean absolute error, we employ the energy within threshold metric. We see signi cant di erences between representations from the evaluation of molecules. We propose ways to improve the performance of atomistic machine learning.
|Date made available
|KAUST Research Repository