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

2024-05-20

arXiv.org

AI Summary

This work addresses the inverse design of metal–organic frameworks (MOFs) by targeting two critical performance metrics—pore volume and CO₂ Henry’s constant—using quantum natural language processing (QNLP), a paradigm previously unexplored in MOF design. Method: We propose quantum-circuit-compatible multi-class classification and generative models, leveraging quantum state encoding for structural representation. A bag-of-words representation is validated on the IBM Qiskit simulator for MOF characterization. Contribution/Results: Our QNLP-driven framework achieves 88.6% and 78.0% accuracy in binary classification for pore volume and CO₂ Henry’s constant, respectively; multi-class classification attains mean accuracies of 92% and 80%; and targeted MOF generation reaches 93.5% and 87% accuracy. This constitutes the first integrated QNLP framework for MOF inverse design, unifying quantum representation, classification, and generation—outperforming classical DisCoCat and sequential models.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: Generation
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 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 93.5% for pore volume and 87% 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.

Problem

• Inverse design MOFs with targeted properties using QNLP
• Compare QNLP models to process MOF datasets effectively
• Develop quantum-based classification models for MOF property prediction

Innovation

• Quantum NLP for MOF inverse design
• Bag-of-words model achieves highest accuracy
• Multi-class classification with quantum circuits