基于大语言模型和AutoML的一次性自动化框架:加速多孔碳材料设计和碳捕集优化

IF 9 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Lin Hu, Zhaorong Zhou, Guozhu Jia
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引用次数: 0

摘要

随着全球变暖的挑战不断升级,对碳捕获的需求不断增加,机器学习已经成为一种流行的方法。传统的机器学习方法面临着诸如缺乏可移植性和复杂操作等挑战,这些挑战阻碍了有效的全局优化。为了解决这一限制,我们提出了一个一次性自动化框架,旨在加速碳捕获材料的开发并优化碳捕获过程。它集实验设计、数据收集、模型训练和结果分析于一体。通过利用大型语言模型(LLM)进行文本挖掘和主动学习,我们将数据集扩展到10,000多个条目。H2O AutoML实现了材料设计和合成的最优模型识别,实现了R2 >;>;0.95和F1得分>;>;0.8. 在数小时内,超过100种优化材料设计产生了超过90%的准确性,代表了实验设计空间的十倍扩展。对比分析显示,与传统方法相比,效率提高了60-100倍。它实现了卓越的碳捕获效率(7.5 mmol/g-9.5 mmol/g)和多孔碳合成收率(0.95),大大超过了目前的基准。这些结果证明了One-Shot自动化框架在指导智能工业碳捕获过程方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A one-shot automated framework based on large language model and AutoML: Accelerating the design of porous carbon materials and carbon capture optimization

A one-shot automated framework based on large language model and AutoML: Accelerating the design of porous carbon materials and carbon capture optimization

A one-shot automated framework based on large language model and AutoML: Accelerating the design of porous carbon materials and carbon capture optimization
With the escalating challenges of global warming and increasing demand for carbon capture, machine learning has become a popular approach. Traditional machine learning methods face challenges such as a lack of portability and complex operations, which prevent effective global optimization. To address this limitation, we present a one-shot automated framework, designed to accelerate the development of carbon capture materials and optimize carbon capture processes. It integrates experimental design, data collection, model training, and result analysis. By leveraging the large language model (LLM) for text mining and active learning, we expanded our dataset to over 10,000 entries. H2O AutoML enabled optimal model identification for material design and synthesis, achieving R2 > 0.95 and F1 Score > 0.8. More than 100 optimized material designs with over 90% accuracy were generated within hours, representing a tenfold expansion of the experimental design space. Comparative analysis revealed a 60-100 fold increase in efficiency over conventional methods. It achieved exceptional carbon capture efficiency (7.5 mmol/g–9.5 mmol/g) and porous carbon synthesis yield (0.95), significantly exceeding current benchmarks. These results demonstrate the One-Shot automated framework’s potential for guiding intelligent industrial carbon capture processes.
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来源期刊
Separation and Purification Technology
Separation and Purification Technology 工程技术-工程:化工
CiteScore
14.00
自引率
12.80%
发文量
2347
审稿时长
43 days
期刊介绍: Separation and Purification Technology is a premier journal committed to sharing innovative methods for separation and purification in chemical and environmental engineering, encompassing both homogeneous solutions and heterogeneous mixtures. Our scope includes the separation and/or purification of liquids, vapors, and gases, as well as carbon capture and separation techniques. However, it's important to note that methods solely intended for analytical purposes are not within the scope of the journal. Additionally, disciplines such as soil science, polymer science, and metallurgy fall outside the purview of Separation and Purification Technology. Join us in advancing the field of separation and purification methods for sustainable solutions in chemical and environmental engineering.
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