549 citations · 1.7k across the 44 of their papers we have counts for
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Training-Free Active Learning Framework in Materials Science with Large Language Models
Hongchen Wang, Rafael Espinosa Castañeda, Jay R. Werber +3
Active learning (AL) accelerates scientific discovery by prioritizing the most informative experiments, but traditional machine learning (ML) models used in AL suffer from cold-sta…
Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
Benhour Amirian, Ashley S. Dale, Sergei Kalinin +1
Accelerated material discovery increasingly relies on artificial intelligence and machine learning, collectively termed "AI/ML". A key challenge in using AI is ensuring that human…
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…
Assessment of different loss functions for fitting equivalent circuit models to electrochemical impedance spectroscopy data
Ali Jaberi, Amin Sadeghi, Runze Zhang +5
Electrochemical impedance spectroscopy (EIS) data is typically modeled using an equivalent circuit model (ECM), with parameters obtained by minimizing a loss function via nonlinear…
Developing and Validating a High-Throughput Robotic System for the Accelerated Development of Porous Membranes
Hongchen Wang, Sima Zeinali Danalou, Jiahao Zhu +7
The development of porous polymeric membranes remains a labor-intensive process, often requiring extensive trial and error to identify optimal fabrication parameters. In this study…
Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images
Sima Zeinali Danalou, Hooman Chamani, Arash Rabbani +3
A major limitation of two-dimensional scanning electron microscopy (SEM) in imaging porous membranes is its inability to resolve three-dimensional pore architecture and interconnec…
Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization
Shayan S. Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius +3
As demand for high-purity lithium surges with the growth of the electric vehicle (EV) industry, cost-effective extraction from lower-grade North American sources like the Smackover…
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…
Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
Yutong Liu, Mehrad Ansari, Robert Black +1
Machine learning and high-throughput experimentation have greatly accelerated the discovery of mixed metal oxide catalysts by leveraging their compositional flexibility. However, t…