549 citations · 1.7k across the 44 of their papers we have counts for
46 papers
AutoRASOR: Autonomous Rapid Scanning Electron Microscope Operator
Kevin Zhang, Mohammad Taha, Rafael Espinosa Castañeda +7
Scanning Electron Microscopy (SEM) is a foundational technique for characterizing material microstructure, which dictates many fundamental physical and chemical properties. With th…
Accelerating Electrochemical Impedance Spectroscopy Measurements by Reducing Reliance on Noisy Low-Frequency Data
Qiuyu Shi, Naohiro Fujinuma, Yonatan Kurniawan +4
Electrochemical impedance spectroscopy (EIS) is a powerful tool for probing kinetic and transport processes in electrochemical systems, but its practical use is often limited by th…
Physics-informed time-series forecasting of perovskite photoluminescence stability
Alexander Wieczorek, Manuel Kober-Czerny, Fábio Lopes +5
Accelerated ageing using elevated temperatures and illumination is one of the most common methods to rapidly study the stability of novel semiconductor materials. However, as the p…
Building a physics-aware AI ecosystem for solid-state hydrogen storage materials
Seong-Hoon Jang, Yiwen Yao, Chuanyu Liu +66
Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evo…
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes proper…
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…
Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS
Alexander Wieczorek, Nathan Rodkey, Jan Sommerhäuser +2
High-power impulse magnetron sputtering (HiPIMS) offers considerable control over ion energy and flux, making it invaluable for tailoring the microstructure and properties of advan…
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…
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers +1
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based ma…
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi +17
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Disco…
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Hongchen Wang, Kangming Li, Scott Ramsay +3
Large Language Models (LLMs) have the potential to revolutionize scientific research, yet their robustness and reliability in domain-specific applications remain insufficiently exp…
An Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis: A Bayesian Approach to Electrochemical Impedance Spectroscopy
Runze Zhang, Debashish Sur, Kangming Li +5
Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of a metallic materials. The analysis of EIS hinges on the selection of an appropriate e…
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,…
High-Tc superconductor candidates proposed by machine learning
Siwoo Lee, Jason Hattrick-Simpers, Young-June Kim +1
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced com…
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost +2
High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing densit…