A5078813738- Advanced Battery Technologies Research
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Advanced Clustering Algorithms Research
- Crystallography and molecular interactions
- Metal-Organic Frameworks: Synthesis and Applications
- Complex Network Analysis Techniques
- Machine Learning in Materials Science
- Reliability and Maintenance Optimization
- Human Mobility and Location-Based Analysis
Stanford University
2024
National Renewable Energy Laboratory
2024
University of California, Davis
2021
Clustering algorithms are a class of unsupervised machine learning (ML) that feature ubiquitously in modern data science, and play key role many learning-based application pipelines. Recently, research the ML community has pivoted to analyzing fairness models, including clustering algorithms. Furthermore, on fair varies widely depending choice algorithm, definitions employed, other assumptions made regarding models. Despite this, comprehensive survey field does not exist. In this paper, we...
Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder metal-organic frameworks. Currently, there are several methods for predicting these using simulations, but the accuracy-efficiency tradeoffs have not been examined depth. In this study, rubrene is used as a model system predict atomic vibrational six different simulation methods: density functional theory, tight binding, binding with Chebyshev polynomial-based...
<title>Abstract</title> Accurate measurement of the variability thermal runaway behavior lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate heat output during using only ejected mass measurements cell metadata, leveraging 139 calorimetry on commercial available from open-access Battery Failure Databank. We show that...
The performance of commercial Li-ion batteries increases year after year. New mechanical designs cells as well changing electrolyte and active material compositions continually push boundaries forward. With such changes, the risks associated with thermal runaway change too a challenge persists in recording benchmarking to determine whether increased comes at expense risk event runaway. Battery Failure Databank provides record mass ejection behaviors past present valuable resource for...