Seeking Metal-Organic Frameworks for hydrogen storage using classical and quantum active learning

IF 2.9 3区化学 Q3 CHEMISTRY, PHYSICAL

Physical Chemistry Chemical Physics Pub Date : 2025-10-01 DOI:10.1039/d5cp02747k

Maicon Pierre Lourenço, Rishabh Shukla, Daya Gaur, Utkarsh Singh, Dennis Salahub, Mosayeb Naseri, Sergey Gusarov

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引用次数: 0

Abstract

Metal-organic frameworks (MOFs) are porous materials with applications from chemical sensing to gas storage and separation. The development of MOFs for hydrogen storage is highly desired. Hydrogen is a clean source of energy and storing it within MOFs depends on their structure, stability, synthesis design and adsorption characteristics. The design space to obtain MOFs with suitable target properties can be aided by artificial intelligence methods that use few data for new discovery, such as active learning (AL) and the recent quantum AL (QAL) method. In this work, AL and QAL methods for MOF design were developed and tested in the search for MOFs and experimental conditions (temperature and pressure) that have enhanced hydrogen storage capability. Our aim is to explore the performance of these techniques in finding this optimum material within a known MOF data set. The AL methods investigated in this work use artificial neural network, support vector regression, classical and quantum Gaussian process (GP and QGP) as regression models for inference. Different uncertainty quantifications for the regression models were considered as well as different acquisition functions for decision making: to select the next MOF to be measured by further "experiments". The QAL performance in finding the optimum material and experimental conditions is reported. QAL uses QGP with a projected quantum kernel with a feature map with entanglement. A network graph method was developed to analyze the AL and QAL performance for MOFs search. The AL and QAL results applied in this known data set indicate that it is possible to search for MOFs with enhanced properties for hydrogen storage with very few data, being able to distinguish the optimum MOF and conditions from similar ones. This finding highlights the potential of classical and quantum "machines" (i.e.: AL and QAL methods) to indicate new MOFs to be synthesized with enhanced adsorption properties.

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利用经典和量子主动学习寻找储氢的金属有机框架

金属有机骨架（mof）是一种多孔材料，从化学传感到气体储存和分离都有广泛的应用。开发用于储氢的MOFs是非常值得期待的。氢是一种清洁能源，在mof中储存氢取决于它们的结构、稳定性、合成设计和吸附特性。利用少量数据进行新发现的人工智能方法，如主动学习（AL）和最近的量子人工智能（QAL）方法，可以帮助获得具有合适目标属性的mof的设计空间。在这项工作中，开发了用于MOF设计的AL和QAL方法，并在寻找具有增强储氢能力的MOF和实验条件（温度和压力）中进行了测试。我们的目标是探索这些技术在已知MOF数据集中找到这种最佳材料的性能。本文研究的人工智能方法使用人工神经网络、支持向量回归、经典高斯过程和量子高斯过程（GP和QGP）作为推理的回归模型。考虑了回归模型的不同不确定性量化以及决策的不同获取函数：通过进一步的“实验”选择下一个要测量的MOF。报道了QAL在寻找最佳材料和实验条件方面的性能。QAL使用QGP和一个带有纠缠的特征映射的投影量子核。提出了一种网络图方法来分析人工智能和QAL在mof搜索中的性能。应用于该已知数据集的AL和QAL结果表明，可以用很少的数据搜索到具有增强储氢性能的MOF，能够从相似的MOF和条件中区分出最佳的MOF。这一发现突出了经典和量子“机器”（即：AL和QAL方法）的潜力，表明可以合成具有增强吸附性能的新型mof。

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来源期刊

Physical Chemistry Chemical Physics 化学-物理：原子、分子和化学物理

CiteScore

5.50

自引率

9.10%

发文量

2675

审稿时长

2.0 months

期刊介绍： Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.