ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning.

MLA

Burner, Jake, et al. “ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning.” Chemistry of Materials, vol. 35, no. 3, Feb. 2023, pp. 900–16. EBSCOhost, https://doi.org/10.1021/acs.chemmater.2c02485.

APA

Burner, J., Luo, J., White, A., Mirmiran, A., Kwon, O., Boyd, P. G., Maley, S., Gibaldi, M., Simrod, S., Ogden, V., & Woo, T. K. (2023). ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning. Chemistry of Materials, 35(3), 900–916. https://doi.org/10.1021/acs.chemmater.2c02485

Chicago

Burner, Jake, Jun Luo, Andrew White, Adam Mirmiran, Ohmin Kwon, Peter G. Boyd, Stephen Maley, et al. 2023. “ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning.” Chemistry of Materials 35 (3): 900–916. doi:10.1021/acs.chemmater.2c02485.