Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

MLA

Deo, Shyam, et al. “Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.” Chemphyschem : A European Journal of Chemical Physics and Physical Chemistry, vol. 25, no. 13, July 2024, p. e202400010. EBSCOhost, https://doi.org/10.1002/cphc.202400010.

APA

Deo, S., Kreider, M. E., Kamat, G., Hubert, M., Zamora Zeledón, J. A., Wei, L., Matthews, J., Keyes, N., Singh, I., Jaramillo, T. F., Abild-Pedersen, F., Burke Stevens, M., Winther, K., & Voss, J. (2024). Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance. Chemphyschem : A European Journal of Chemical Physics and Physical Chemistry, 25(13), e202400010. https://doi.org/10.1002/cphc.202400010

Chicago

Deo, Shyam, Melissa E Kreider, Gaurav Kamat, McKenzie Hubert, José A Zamora Zeledón, Lingze Wei, Jesse Matthews, et al. 2024. “Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.” Chemphyschem : A European Journal of Chemical Physics and Physical Chemistry 25 (13): e202400010. doi:10.1002/cphc.202400010.