Generative Models for Extrapolation Prediction in Materials Informatics

Kan Hatakeyama-Sato*, Kenichi Oyaizu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


We report a deep generative model for regression tasks in materials informatics. The model is introduced as a component of a data imputer and predicts more than 20 diverse experimental properties of organic molecules. The imputer is designed to predict material properties by "imagining"the missing data in the database, enabling the use of incomplete material data. Even removing 60% of the data does not diminish the prediction accuracy in a model task. Moreover, the model excels at extrapolation prediction, where target values of the test data are out of the range of the training data. Such an extrapolation has been regarded as an essential technique for exploring novel materials but has hardly been studied to date due to its difficulty. We demonstrate that the prediction performance can be improved by >30% by using the imputer compared with traditional linear regression and boosting models. The benefit becomes especially pronounced with few records for an experimental property (<100 cases) when prediction would be difficult by conventional methods. The presented approach can be used to more efficiently explore functional materials and break through previous performance limits.

Original languageEnglish
Pages (from-to)14566-14574
Number of pages9
JournalACS Omega
Issue number22
Publication statusPublished - 2021 Jun 8

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


Dive into the research topics of 'Generative Models for Extrapolation Prediction in Materials Informatics'. Together they form a unique fingerprint.

Cite this