Metallic Metal-Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations

Emerging applications of metal-organic frameworks (MOFs) in electronic devices will benefit from the design and synthesis of intrinsically, highly electronically conductive MOFs. However, very few are known to exist. It is a challenging task to search for electronically conductive MOFs within the tens of thousands of reported MOF structures. Using a new strategy (i.e., transfer learning) of combining machine learning techniques, statistical multivoting, and ab initio calculations, we screened 2932 MOFs and identified 6 MOF crystal structures that are metallic at the level of semilocal DFT band theory: Mn₂[Re₆X₈(CN)₆]₄ (X = S, Se,Te), Mn[Re₃Te₄(CN)₃], Hg[SCN]₄Co[NCS]₄, and CdC₄. Five of these structures have been synthesized and reported in the literature, but their electrical characterization has not been reported. Our work demonstrates the potential power of machine learning in materials science to aid in down-selecting from large numbers of potential candidates and provides the information and guidance to accelerate the discovery of novel advanced materials.