765 citations
- National Research Council CanadaCA9 papers
- Geological Survey of CanadaCA6 papers
- University of TorontoCA5 papers
- McGill UniversityCA4 papers
- Carleton UniversityCA3 papers
- Material Measurement LaboratoryUS3 papers
- National Institute of Standards and TechnologyUS3 papers
- University of CalgaryCA3 papers
- University of New BrunswickCA3 papers
- Canadian Forest ServiceCA2 papers
- Schwartz/Reisman Emergency Medicine InstituteCA2 papers
- Space Research InstituteRU2 papers
29 papers
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…
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…
Business Models for Digitalization Enabled Energy Efficiency and Flexibility in Industry: A Survey with Nine Case Studies
Zhipeng Ma, Bo Nørregaard Jørgensen, Michelle Levesque +2
Digitalization is challenging in heavy industrial sectors, and many pi-lot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles fo…
On the redundancy in large material datasets: efficient and robust learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary +3
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Kangming Li, Brian DeCost, Kamal Choudhary +2
Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score…
High-resolution semantically-consistent image-to-image translation
Mikhail Sokolov, Christopher Henry, Joni Storie +3
Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing dataset…