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.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/675576 |
| Date | 25 November 2021 |
| Creators | Yu, Hao |
| Contributors | Schwingenschlögl, Udo, Physical Science and Engineering (PSE) Division, Laquai, Frédéric, Zhang, Xiangliang, Wu, Ying |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2023-02-21, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2023-02-21. |
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