Exploring the expansive and largely untapped chemical space of metal-organic frameworks (MOFs) holds promise for revolutionising the field of materials science. MOFs, hailed for their modular architecture, offer unmatched flexibility in customising functionalities to meet specific application needs. However, navigating this chemical space to identify optimal MOF structures poses a significant challenge. Tradtional high-throughput computational screening (HTCS), while useful, is often limited by a distribution bias towards materials not aligned with the desired functionalities. To overcome these limitations, this study adopts a ”deep dreaming” methodology to optimise MOFs in silico, aiming to generate structures with systematically shifted properties that are closer to target functionalities from the outset. Our methodology integrates property prediction and structure optimisation within a single interpretable framework, leveraging a specialised chemical language model augmented with attention mechanisms. Focusing on a curated set of MOF properties critical to applications like carbon capture and energy storage, our approach not only expands the selection of potential materials for HTCS but also opens new avenues for material exploration and development.

中文翻译：

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使用深度梦想方法设计金属有机框架

探索金属有机框架 (MOF) 广阔且尚未开发的化学空间有望彻底改变材料科学领域。 MOF 因其模块化架构而备受赞誉，在定制功能以满足特定应用需求方面提供了无与伦比的灵活性。然而，探索这个化学空间以确定最佳的 MOF 结构提出了重大挑战。传统的高通量计算筛选（HTCS）虽然有用，但通常受到与所需功能不相符的材料分布偏差的限制。为了克服这些限制，本研究采用“深度梦想”方法在计算机中优化 MOF，旨在从一开始就生成具有系统改变特性的结构，更接近目标功能。我们的方法将属性预测和结构优化集成在一个可解释的框架内，利用通过注意机制增强的专门化学语言模型。我们的方法专注于对碳捕获和能源存储等应用至关重要的一系列 MOF 特性，不仅扩大了 HTCS 潜在材料的选择范围，而且还为材料探索和开发开辟了新途径。