Ethan Eisenberg

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Ethan Eisenberg

Ethan EisenbergEthan EisenbergEthan Eisenberg
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My Journey In Science Research

I was first introduced to science research during my freshman year in high school when I joined the Intro to Science Research class. After, I learned how to ask the right questions in Biotechnology, engaged in the Garcia Summer Scholars program, and interned with Dr. Urban at Columbia University.

LI Herald Article on Perovskite Solar Cell Accomplishment: JSHS Semifinalist

Research Conferences

American Chemical Society

materials research society

materials research society

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View Abstract and Poster Publication

materials research society

materials research society

materials research society

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Please make sure to go on December 5th and scroll down to view my abstract publication. Please note that the project is listed 3rd from the bottom.  Please be advised that in addition to the American Chemical Society (ACS),  I am listed as first author for the Materials Research Society (MRS).  

View Abstract Publication

Perovskite Solar Cell Project

 Perovskites are prepared via spin coating, a solution-based fabrication method. In turn, they are believed to be cheaper to produce than the conventional crystalline silicon solar cells. However, their classical structure is organic, making them susceptible to intense heat, light, and moisture. During my Garcia Summer Scholars program I developed a more stable and efficient perovskite solar cell by implementing a novel mixed cation/halide structure. 


Acknowledgements: I would like to thank Dr. Rafailovich, Yuchen Zhou, Yifan Yin,  and and the Garcia Summer Scholars Program for aiding me in my journey with sustainable energy. 


Below please find the abstract for the project titled Stability Enhancement of Perovskite Solar Cells Using a Mixed Cation/Halide Perovskite 

Download PDF

Completed Perovskite Device

Machine Learning Project

Project title: Developing Machine Learning Models to Predict the Heat Capacities of Metal Oxides at Finite Temperatures 


Heat capacity is an important thermodynamic quantity to evaluate the temperature dependence of entropy and enthalpy and Gibbs free energy. The stability of a material is determined by its Gibbs free energy, and thus the ability to predict free energies (which is possible from predicting the heat capacity)  enables computational materials (and process) design (Dr. Alexander Urban, Columbia University).  In this project, I developed several supervised and unsupervised machine learning models to predict the heat capacity of metal oxides. This project allowed me to combine my two passions for chemistry and computer science to further my quest in scientific research. 


Acknowledgments: I would like to thank Dr. Alexander Urban, Vahe Gharakhanyan, and the Urban Research group for supporting and guiding me in this endeavor. 




python libraries used and models developed

* All code was written in Google Colab, a cloud-based Integrated Development Environment (IDE) 

  • Seaborn for data visualization
  • Matplotlib for data visualization
  • Pandas for data manipulation and analysis  
  • NumPy for mathematical functions 
  • Pymatgen for materials analysis 

Models Developed 

  • Linear Regression 
  • LASSO Regularization 
  • Ridge Regularization 
  • Random Forest Regression
  • K-means Clustering 
  • Principal Component Analysis 




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