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.
Optuna: A Next-generation Hyperparam- eter Optimization Framework
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.