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 hypot...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.展开更多
基金National Research Foundation of Korea(Project Number RS-2024-00337004)for the financial support.
文摘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.
