Publications, Activities, and Awards
- Ahead by a Century: Discovery of Laves Phases Assisted by Machine Learning
- Can we use machine learning to find synthesizable compounds?
- Discovery of Li-containing compounds with channel structures guided by machine learning
- Explainable machine learning in materials chemistry: Decision trees as scoring function
- Interpretable machine learning in solid state chemistry, with applications to perovskites, spinels, and rare-earth intermetallics: Finding descriptors using decision trees
- Machine-learning classification of Laves phases and prediction of new compounds
- Machine-learning prediction of new Laves phases with experimental validation
- Materials discovery of intermetallics through machine learning: Experimental validation and interpretable models
- Materials discovery through machine learning: Experimental validation and interpretable models
- Predicting noncentrosymmetric quaternary tellurides using machine learning
- Predicting thermoelectric figures-of-merit for half-Heusler compounds using machine learning
- Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX
- Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX
- Revisiting the classification of spinels through machine learning
- Searching for new spinels using machine learning
- Searching for new spinels using machine learning