ICAMS / Interdisciplinary Centre for Advanced Materials Simulation

Publications

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

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

npj Computational Materials, 5, 111 (1-11), (2019)

Architecture of the machine-learning model that reached the second place in the NOMAD 2018 Kaggle competition on predicting the formation energy and the band gap of Al-Ga-In-O compounds for optoelectronic applications. (Available via license: CC BY 4.0.

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 (Al x Ga y In 1–x–y ) 2 O 3 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.


Keyword(s): BOP, machine learning, transparent conductors, NOMAD, kaggle
Cite as: https://www.nature.com/articles/s41524-019-0239-3
DOI: 10.1038/s41524-019-0239-3
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