A machine-learning model trained on DFT data predicts bond lengths from local coordination to screen 1.175 million transition-metal oxides and fluorides for low volume change upon ion intercalation.
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets.PLOS ONE2015,10, e0118432
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
High-Throughput-Screening Workflow for Predicting Volume Changes by Ion Intercalation in Battery Materials
A machine-learning model trained on DFT data predicts bond lengths from local coordination to screen 1.175 million transition-metal oxides and fluorides for low volume change upon ion intercalation.