ArticleACS omega2021
Generative Models for Extrapolation Prediction in Materials Informatics.
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Attention-enhanced variational learning for physically informed discovery of exceptionally hard multicomponent bulk metallic glasses.Nature communications · 2026Article
- Uncertainty-Quantified Primary Particle Size Prediction in Li-Rich NCM Materials via Machine Learning and Chemistry-Aware Imputation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- TCM-navigator, a deep learning-based workflow for generation and evaluation of traditional Chinese medicine-like compounds for drug development.Briefings in bioinformatics · 2025Article
- Accelerated Development of Novel Biomass-Based Polyurethane Adhesives via Machine Learning.ACS applied materials & interfaces · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.