112 citations · 198 across the 6 of their papers we have counts for
6 papers
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
Daniel Persaud, Mike Werezak, Mark Xu +9
Corrosion testing is slow, labor-intensive, and sensitive to operator technique, limiting the generation of large, high-quality datasets for data-driven materials discovery. The Ma…
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…
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…
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…
Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'
Daniel Persaud, Logan Ward, Jason Hattrick-Simpers
The integration of machine learning techniques in materials discovery has become prominent in materials science research and has been accompanied by an increasing trend towards ope…
On the redundancy in large material datasets: efficient and robust learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary +3
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…