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Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning.
Is your composition space markedly different from the environment that inspires you to write poetry in the first place?
To further characterize the predictions of ElemNet, we analyzed how the predictions are distributed across composition space.
Composition space analysis was performed through approximately 30 reactions using each amine under mild hydrothermal conditions.
Distillation boundaries are created by saddle azeotropes and divide the composition space into distillation regions.
Explanation is provided based on the visualization of selectivity lines in the composition space.
As our deep learning model can make robust and fast predictions, it can be used to perform combinatorial screening in huge composition space for discovery of new materials.
The micromixing term in the PDF transport equations, representing diffusion in composition space, is however unclosed.
Modeling aspects of the a priori PDF, accounting for the bias in composition space, are discussed.
We show that this mathematical artifact is found outside the feasible composition space.
The PCs are derived from a priori principal component analysis (PCA) of the same composition space.
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