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Graph-based Machine Learning Beyond Stable Materials and Relaxed Crystal Structures by Ekström, Filip; Armiento, Rickard; Lindsten, Fredrik is a scholarly article available to read on EtoBox.

What is Graph-based Machine Learning Beyond Stable Materials and Relaxed Crystal Structures about?

There has been a recent surge of interest in using machine learning to approximate density functional theory (DFT) in materials science. However, many of the most performant models are evaluated on large databases of computed properties of, primarily, materials with precise atomic coordinates available, and which have been experimentally synthesized, i.e., which are thermodynamically stable or metastable. These aspects provide challenges when applying such models on theoretical candidate materials, for example for materials discovery, where the coordinates are not known. To extend the scope of this methodology, we investigate the performance of the Crystal Graph Convolutional Neural Network (CGCNN) on a data set of theoretical structures in three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many highly unstable structures. We then investigate the impact on the performance of using atomic positions that are only partially relaxed into local energy minima. We also explore options for improving the performance in these scenarios by transfer learning, either from models trained on a large database of mostly stable systems, or a different but related phase diagram.

Author

Ekström, Filip; Armiento, Rickard; Lindsten, Fredrik

Published

2021

Language

EN