137 citations · 440 across the 16 of their papers we have counts for
3 papers · 1 filter
High-throughput validation of phase formability and simulation accuracy of Cantor alloys
Changjun Cheng, Daniel Persaud, Kangming Li +7
High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast composit…
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
Ashley S. Dale, Kangming Li, Brian DeCost +4
Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty…
Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
Qiuyu Shi, Kangming Li, Yao Fehlis +4
Self-driving laboratories (SDLs) have shown promise to accelerate materials discovery by integrating machine learning with automated experimental platforms. However, errors in the…