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

作　　者：Christopher Sutton Luca M.Ghiringhelli Takenori Yamamoto Yury Lysogorskiy Lars Blumenthal Thomas Hammerschmidt Jacek R.Golebiowski Xiangyue Liu Angelo Ziletti Matthias Scheffler 

机构地区：[1]Fritz Haber Institute of the Max Planck Society,Berlin,Germany [2]Research Institute for Mathematical and Computational Sciences(RIMCS),Yokohama,Japan [3]ICAMS,Ruhr-Universität Bochum,Bochum,Germany [4]EPSRC Centre for Doctoral Training on Theory and Simulation of Materials,Department of Physics,Imperial College London,London,UK [5]Thomas Young Centre for Theory and Simulation of Materials,Department of Materials,Imperial College London,London,UK

出　　处：《npj Computational Materials》2019年第1期150-160,共11页计算材料学（英文）

基　　金：The project received funding from the European Union’s Horizon 2020 research and innovation program(grant agreement no.676580);the Molecular Simulations from First Principles(MS1P).C.S.gratefully acknowledges funding by the Alexander von Humboldt Foundation.

摘　　要：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) compounds.Its 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 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.

关 键 词：energy prediction OVERLAP 

分 类 号：TG1[金属学及工艺—金属学]