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

npj Computational Materials 2019年第1期5卷 150-160页

作者： Christopher Sutton Luca M.Ghiringhelli Takenori Yamamoto Yury Lysogorskiy lars blumenthal Thomas Hammerschmidt Jacek R.Golebiowski Xiangyue Liu Angelo Ziletti Matthias Scheffler Fritz Haber Institute of the Max Planck Society BerlinGermany Research Institute for Mathematical and Computational Sciences(RIMCS) YokohamaJapan ICAMS Ruhr-Universität BochumBochumGermany EPSRC Centre for Doctoral Training on Theory and Simulation of Materials Department of PhysicsImperial College LondonLondonUK Thomas Young Centre for Theory and Simulation of Materials Department of MaterialsImperial College LondonLondonUK

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)GayIn_(1-x-y))_(2)O_(3) *** aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation ***,we present a summary of the top-three ranked ML *** first-place solution was based on a crystal-graph representation that is novel for the ML of properties of *** 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 *** third-place model employed the smooth overlap of atomic position representation with a neural *** 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 *** relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy.

关键词： energy prediction overlap

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