19 papers
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…
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…
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…
Building informative materials datasets beyond targeted objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making the reuse and long-term utility of datasets critical important for future discovery campaigns. In practice, researchers p…
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…