Accelerated Discovery of Quaternary Heusler with High-Throughput Density Functional Theory and Machine Learning

Discovering many-component crystalline materials is a complex task owing to the large composition space. Here, we employ machine learning to find 55 previously unknown, thermodynamically stable quaternary Heusler (QH) compounds in a search space of over 2M compounds after performing only 303 Density Functional Theory (DFT) calculations. Our ML model predicts the stability of a material based on attributes derived from the Voronoi tessellation of its crystal structure, and we trained the model using 450k entries from the OQMD. We find that including data from many types of crystal structures when training ML models leads to better accuracy than when using a carefully curated dataset containing only a single family of material (i.e., only Heuslers). This means that large datasets, such as OQMD, are particularly valuable for materials discovery. We also find that the models trained using our method perform 10x better at identifying new stable QHs than existing heuristics and about 30% better than other machine learning methods. Given the fact that our method does not require a specially-developed training set and its excellent performance, we propose it can be used to discover materials with many other types of crystal structures.