Machine Learning-Assisted Performance Prediction and Design of MXene Materials
Here, reported ML-assisted studies on MXene prediction and design are organized by target property and workflow, and ML–DFT screening, graph neural networks, uncertainty quantification, active learning, and interpretability are examined.
Ling-Hong Lu, Qian-Kun Li, Xin-Chen Wang et al.
· Smart Chemical Engineering · 0 citations