Efficient CO2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO2. However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiting the exploration of novel solutions. Here, SAGE-Amine was introduced, a generative modeling approach that integrates Scoring-Assisted Generative Exploration (SAGE) with quantitative structure-property relationship models to design new amines tailored for CO2 capture. Unlike conventional virtual screening restricted to existing compounds, SAGE-Amine generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets. SAGE-Amine identified known amines for CO2 capture from scratch and successfully performed single-property optimization, increasing basicity or reducing viscosity or vapor pressure. Furthermore, it facilitated multi-property optimization, simultaneously achieving high basicity with low viscosity and vapor pressure. The 10 top-ranked amines were suggested using SAGE-Amine and their thermodynamic properties were further assessed using COSMO-RS simulations, confirming their potential for CO2 capture. These results highlight the potential of generative modeling in accelerating the discovery of amine solvents and expanding the possibilities for industrial CO2 capture applications.

翻译：高效的二氧化碳捕集对于缓解气候变化至关重要，胺基溶剂因其与二氧化碳的强反应性而被广泛应用。然而，优化关键属性如碱性、粘度和吸收容量仍具挑战性，因为传统方法依赖于劳动密集型的实验和预定义的化学数据库，限制了新解决方案的探索。本文介绍了SAGE-Amine，这是一种生成式建模方法，它将评分辅助生成探索与定量结构-性质关系模型相结合，以设计用于二氧化碳捕集的新型胺类。与局限于现有化合物的传统虚拟筛选不同，SAGE-Amine利用在胺类数据集上训练的自回归自然语言处理模型生成新型胺类。SAGE-Amine从零开始识别出已知的用于二氧化碳捕集的胺类，并成功执行了单属性优化，提高了碱性或降低了粘度或蒸汽压。此外，它促进了多属性优化，同时实现了高碱性与低粘度和低蒸汽压。使用SAGE-Amine提出了10种排名最高的胺类，并利用COSMO-RS模拟进一步评估了它们的热力学性质，证实了它们在二氧化碳捕集方面的潜力。这些结果凸显了生成式建模在加速胺类溶剂发现和拓展工业二氧化碳捕集应用可能性方面的潜力。