Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing

arXiv:2405.11783v1 Announce Type: new
Abstract: In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 150 hypothetical MOF structures consisting of 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and $H_{2}$ uptake values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective …

abstract arxiv cs.ai cs.cl cs.lg design explore frameworks language language processing ligands metal natural natural language natural language processing nodes potential processing quant-ph quantum study type