利用量子自然语言处理进行具有特定性质的金属有机框架的反向设计
Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing
AI总结：
本研究利用量子自然语言处理反向设计金属有机框架，通过词袋模型实现高准确率的性质预测。
摘要：
在本研究中，我们探讨了利用量子自然语言处理（QNLP）来反向设计具有目标性质的金属有机框架（MOFs）的潜力。具体而言，通过分析由3种拓扑结构、10种金属节点和15种有机配体组成的450个假设MOF结构，我们将这些结构分为四个不同的类别，用于孔隙体积和CO₂亨利常数值。随后，我们比较了各种QNLP模型（即词袋模型、DisCoCat（分布组成分类）和基于序列的模型）以确定最有效的处理MOF数据集的方法。使用IBM Qiskit提供的经典模拟器，词袋模型被确定为最优模型，在孔隙体积和CO₂亨利常数的二分类任务上分别实现了88.6%和78.0%的验证准确率。进一步，我们开发了针对量子电路概率性质的多类分类模型，孔隙体积和CO₂亨利常数数据集在不同类别上的平均测试准确率分别为92%和80%。最后，生成具有目标性质的MOF的性能在孔隙体积上达到93.5%的准确率，在CO₂亨利常数上达到87%的准确率。尽管我们的研究仅涵盖了MOF搜索空间的一小部分，但这一发现标志着利用量子计算进行材料设计的有希望的第一步，为探索MOFs的复杂景观提供了新的视角。
英文摘要：
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.
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