Bibliography

CCH+14

Yongchul G. Chung, Jeffrey Camp, Maciej Haranczyk, Benjamin J. Sikora, Wojciech Bury, Vaiva Krungleviciute, Taner Yildirim, Omar K. Farha, David S. Sholl, and Randall Q. Snurr. Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals. Chemistry of Materials, 26(21):6185–6192, November 2014. URL: http://pubs.acs.org/doi/10.1021/cm502594j, doi:10.1021/cm502594j.

Hes06

J. R. Hester. A validating CIF parser: \textit PyCIFRW. Journal of Applied Crystallography, 39(4):621–625, August 2006. URL: http://scripts.iucr.org/cgi-bin/paper?S0021889806015627, doi:10.1107/S0021889806015627.

Kab76

W Kabsch. A solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, A32:922–923, 1976. URL: http://journals.iucr.org/a/issues/1976/05/00/a12999/a12999.pdf, doi:10.1107/S0567739476001873.

Kab78

W Kabsch. A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallographica Section A, A34:827–828, 1978. URL: http://journals.iucr.org/a/issues/1978/05/00/a15629/a15629.pdf, doi:10.1107/S0567739478001680.

Kro19

Jimmy Charnley Kromann. Calculate Root-mean-square deviation (RMSD) of two molecules, using rotation, in xyz or pdb format: charnley/rmsd. March 2019. original-date: 2013-04-24T22:46:14Z. URL: https://github.com/charnley/rmsd.

MAC+18

Bryce Meredig, Erin Antono, Carena Church, Maxwell Hutchinson, Julia Ling, Sean Paradiso, Ben Blaiszik, Ian Foster, Brenna Gibbons, Jason Hattrick-Simpers, Apurva Mehta, and Logan Ward. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Molecular Systems Design & Engineering, 3(5):819–825, 2018. URL: http://xlink.rsc.org/?DOI=C8ME00012C, doi:10.1039/C8ME00012C.

MLW+17

Peyman Z. Moghadam, Aurelia Li, Seth B. Wiggin, Andi Tao, Andrew G. P. Maloney, Peter A. Wood, Suzanna C. Ward, and David Fairen-Jimenez. Development of a Cambridge Structural Database Subset: A Collection of Metal–Organic Frameworks for Past, Present, and Future. Chemistry of Materials, 29(7):2618–2625, April 2017. URL: https://doi.org/10.1021/acs.chemmater.7b00441, doi:10.1021/acs.chemmater.7b00441.

NCC+17

Dalar Nazarian, Jeffrey S. Camp, Yongchul G. Chung, Randall Q. Snurr, and David S. Sholl. Large-Scale Refinement of Metal-Organic Framework Structures Using Density Functional Theory. Chemistry of Materials, 29(6):2521–2528, March 2017. URL: https://doi.org/10.1021/acs.chemmater.6b04226, doi:10.1021/acs.chemmater.6b04226.

PeyrePechaudKC10

Gabriel Peyré, Mickael Péchaud, Renaud Keriven, and Laurent D. Cohen. Geodesic Methods in Computer Vision and Graphics. Now Publishers Inc, December 2010. ISBN 978-1-60198-396-1. Google-Books-ID: XuuVsQ7XOl8C.

SHK+19

Arni Sturluson, Melanie T Huynh, Alec R Kaija, Caleb Laird, Feier Hou, Zhenxing Feng, Christopher E Wilmer, Yamil J Colon, Yongchul G Chung, Daniel W Siderius, and Cory M Simon. The role of molecular modeling & simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation. ChemRxiv, pages 56, 2019. doi:10.26434/chemrxiv.7854980.v1.

TFT17

Trevor Tibshirani, Jerome Friedman, and Robert Tibshirani. The Elements of Statistical Learning - Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, 2 edition, January 2017. URL: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf.

TLYZ17

Minman Tong, Youshi Lan, Qingyuan Yang, and Chongli Zhong. Exploring the structure-property relationships of covalent organic frameworks for noble gas separations. Chemical Engineering Science, 168:456–464, August 2017. URL: https://linkinghub.elsevier.com/retrieve/pii/S000925091730310X, doi:10.1016/j.ces.2017.05.004.

ZPM19

Pezhman Zarabadi-Poor and Radek Marek. Comment on \textquotedblleft Database for CO $_\textrm 2$ Separation Performances of MOFs Based on Computational Materials Screening\textquotedblright . ACS Applied Materials & Interfaces, pages acsami.8b15684, March 2019. URL: http://pubs.acs.org/doi/10.1021/acsami.8b15684, doi:10.1021/acsami.8b15684.