The Rolston Lab at Arizona State University studies the long-term stability, reliability, and degradation mechanisms of emerging energy materials and devices.
Our research spans perovskites, photovoltaics, batteries, and energy technologies operating in demanding environments, including terrestrial and space applications. We combine materials science, automated experimentation, electrical and electrochemical characterization, data-driven modeling, optimization, and machine learning to understand how materials and devices evolve over time, why they fail, and how they can be made more reliable.
This GitHub organization hosts the lab's open-source software and research code, including measurement and automation tools, experimental data pipelines, impedance-analysis workflows, machine-learning models, and optimization methods developed alongside our experimental research.
We study the stability, degradation mechanisms, processing, and reliability of perovskite-based materials and devices across a range of applications and operating environments.
This includes terrestrial photovoltaics, space-relevant applications, accelerated aging, device characterization, processing, and the development of measurement and analysis methods that help connect changes in material properties to device performance.
A central goal is to understand how perovskite materials and interfaces evolve under realistic and extreme conditions and to use that understanding to enable more stable and reliable technologies.
We study degradation and health evolution in electrochemical energy-storage systems using cycling data, electrochemical impedance spectroscopy (EIS), and data-driven analysis.
Our work includes reproducible workflows that connect raw instrument data to physical and statistical descriptors, degradation trends, state-of-health prediction, and machine-learning models.
Many of the experimental and computational methods used across these research areas are shared.
Techniques such as impedance spectroscopy, accelerated aging, automated measurement, degradation modeling, Bayesian optimization, and machine learning can be applied across different material and device systems.
By combining experimental measurements with computational tools, we aim to identify transferable descriptors of degradation and develop more efficient approaches for understanding and improving energy-material reliability.
| Project | Area | Description |
|---|---|---|
| PixelMux | Perovskites / PV | Automated multi-pixel solar-cell characterization platform for high-throughput IV measurements using a Keithley 2460 SMU and relay multiplexing |
| Perovskite-EIS | Perovskites | Analysis workflows for electrochemical impedance spectroscopy of perovskite devices |
| Battery-Data-Visualizer | Batteries | Browser-based visualization and analysis of BioLogic EC-Lab .mpt files, including impedance plots, cycle-level trends, and data export |
| LIB-EIS-ML | Batteries / ML | Machine-learning workflows connecting lithium-ion battery impedance measurements with degradation and state-of-health |
| BO-for-Energy-material | Energy Materials | Bayesian optimization methods for accelerating experimental exploration and optimization of energy-material systems |
Other repositories in this organization contain ongoing research, experimental tools, analysis workflows, and forks used by the lab.
Much of the software here is developed directly alongside ongoing experiments. Repositories may therefore evolve as measurement systems, datasets, scientific questions, and analysis approaches change.
Our goal is to make research workflows increasingly reproducible, reusable, and transferable across experiments and material systems—from instrument control and raw-data processing to physical interpretation and predictive modeling.
- Lab: Rolston Lab, Arizona State University
- Website: https://rolston.lab.asu.edu/
- GitHub: https://github.com/rolston-lab-asu
