Computational and Machine Learning Methods for CO Capture Using Metal-Organic Frameworks.

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO adsorption.