Publications

Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition

Sutton, C. and Ghiringhelli, L.M. and Yamamoto, T. and Lysogorskiy, Y. and Blumenthal, L. and Hammerschmidt, T. and Golebiowski, J.R. and Liu, X. and Ziletti, A. and Scheffler, M.

NPJ COMPUTATIONAL MATERIALS
Volume: 5 Pages:
DOI: 10.1038/s41524-019-0239-3
Published: 2019

Abstract
A public data-analytics competition was organized by the Novel Materials Discovery (NOMAD) Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000 (AlxGayIn1–x–y)2O3 compounds. Its aim was to identify the best machine-learning (ML) model for the prediction of two key physical properties that are relevant for optoelectronic applications: the electronic bandgap energy and the crystalline formation energy. Here, we present a summary of the top-three ranked ML approaches. The first-place solution was based on a crystal-graph representation that is novel for the ML of properties of materials. The second-place model combined many candidate descriptors from a set of compositional, atomic-environment-based, and average structural properties with the light gradient-boosting machine regression model. The third-place model employed the smooth overlap of atomic position representation with a neural network. The Pearson correlation among the prediction errors of nine ML models (obtained by combining the top-three ranked representations with all three employed regression models) was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model performance. Ensembling relatively decorrelated models (based on the Pearson correlation) leads to an even higher prediction accuracy. © 2019, The Author(s).

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