Figure 5 | Scientific Reports

Figure 5

From: Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Figure 5

Correlating void structure to MOF property. (a) str-m4-o14-acs-sym-8 (b) str-m4-o1-o22-acs-sym-94 (c) str-m4-o1-o24-acs-sym-96 (d) str-m4-o1-o24-acs-sym-165. The representative cycles of voids corresponding to the void most correlated with the CO\(_2\) Henry’s coefficient in example MOFs with high CO\(_2\) Henry’s coefficients. The voids are all composed of a similar bonding structure, with each different atom type represented by a different color. As noted in33, the process of identifying the void structure that appears in top performing MOFs can be extremely time-consuming via manually detected features. Thus, we hope that our much faster and topologically—grounded approach will allow for further study in pinpointing the channel and void shapes and bonding structures that correlate best to important material’s properties, thereby encouraging the targeted design of structures to maximize desirable properties. Figure created with VisIt 3.1.4 (https://wci.llnl.gov/simulation/computer-codes/visit).

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