Property-guided inverse design of metal-organic frameworks using quantum natural language processing

In this study, we explore the potential of using quantum natural language processing (QNLP) for property-guided inverse design of metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of 3 topologies, 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and CO 2 Henry’s constant 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 approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 88.6% and 78.0% for binary classification tasks on pore volume and CO 2 Henry’s constant, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 92% and 80% across different classes for pore volume and CO 2 Henry’s constant datasets. Finally, the performance of generating MOF with target properties showed accuracies of 97.75% for pore volume and 90% for CO 2 Henry’s constant, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs.