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您的检索式：作者名="Elsa Olivetti"
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| 1 | Virtual screening of inorganic materials synthesis parameters with deep learning显示文摘Virtual materials screening approaches have proliferated in the past decade,driven by rapid advances in first-principles computational techniques,and machine-learning algorithms.By comparison,computationally driven materials synthesis screening is still in its infancy,and is mired by the challenges of data sparsity and data scarcity:Synthesis routes exist in a sparse,highdimensional parameter space that is difficult to optimize over directly,and,for some materials of interest,only scarce volumes of literature-reported syntheses are available.In this article,we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes.We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space,which is found to improve the performance of machine-learning tasks.To realize this screening framework even in cases where there are few literature data,we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems.We apply this variational autoencoder framework to generate potential SrTiO_(3) synthesis parameter sets,propose driving factors for brookite TiO_(2) formation,and identify correlations between alkali-ion intercalation and MnO_(2) polymorph selection. | Edward Kim Kevin Huang Stefanie Jegelka Elsa Olivetti | 2017 | npj Computational Materials2017，，1： | 14 |
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