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arXiv 2602.22706cond-mat.mtrl-sci
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基于大语言模型的碳同素异形体发现及其键网络熵

LLM-driven discovery for carbon allotropes with bond-network entropy

Xi’an Jiaotong University(西安交通大学);AiMaterials Research LLC(AiMaterials研究有限责任公司)

Yuzhou Hao, Yujie Liu, Xuejie Li, Turab Lookman, Xiangdong Ding, Jun Sun, Zhibin Gao

AI总结:

利用大语言模型和机器学习势能结合,发现具有混合sp-sp³杂化的新型碳同素异形体,展现极端热各向异性和超硬特性。

摘要:

新型碳同素异形体的发现对于先进热管理至关重要。然而,使用从头计算探索碳的广阔构型空间仍具有计算上不可行性。受碳丰富的拓扑景观驱动,其中sp、sp²和sp³杂化状态之间的竞争决定了材料性能,我们建立了一个闭环人工智能框架来探索这个复杂的构型空间。我们引入了杂化熵描述符来指导搜索超越传统形式。在此,我们建立了一个闭环人工智能框架,使大型语言模型(LLM)用于结构生成与机器学习势能(MLP)用于加速评估相结合。利用CrystaLLM生成候选者,并用迭代优化的MLP进行高保真度验证,我们筛选了数千种结构以识别几种稳定的同素异形体,具有异常的性能。具体而言,我们报告了“yne-diamond C₁₂”和“yne-hex-diamond C₈”,其极端热各向异性和极低的平面剪切刚度源于其混合的sp-sp³杂化。此外,我们发现了一种复杂的sp-sp²-sp³杂化的C₁₂相,结合了金属导电性和异常的负泊松比。值得注意的是,我们识别出了一种超硬相(C16_3),其计算得出的维氏硬度(103.3 GPa)超过了钻石(96 GPa)。微观分析显示,这些材料的热传输由刚性框架和柔性连接器之间的相互作用所支配。本工作扩展了已知的碳相空间,并展示了将生成式人工智能与机器学习势能相结合用于功能材料逆向设计的有效性。

英文摘要:

The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains computationally prohibitive. Driven by the rich topological landscape of carbon, where the competition between $sp, sp^2,$ and $sp^3$ hybridization states dictates material performance, we establish a closed-loop AI framework to explore this complex configurational space. We introduce a hybridization entropy descriptor to guide the search beyond conventional forms. Here, we establish a closed-loop AI framework that synergizes a Large Language Model (LLM) for structural generation with a Machine Learning Potential (MLP) for accelerated evaluation. Leveraging CrystaLLM to generate candidates and an iteratively refined MLP for high-fidelity validation, we screened thousands of structures to identify several stable allotropes with exotic properties. Specifically, we report ``yne-diamond C$_{12}$'' and ``yne-hex-diamond C$_{8}$'', which exhibit extreme thermal anisotropy and ultralow in-plane shear stiffness arising from their mixed $sp$-$sp^3$ hybridization. Furthermore, we discovered a complex $sp$-$sp^2$-$sp^3$ hybridized C$_{12}$ phase that combines metallic conductivity with an anomalous negative Poisson's ratio. Notably, we identified a superhard phase (C16_3) possessing a calculated Vickers hardness (103.3 GPa) exceeding that of diamond 96 GPa). Microscopic analysis reveals that thermal transport in these materials is governed by the interplay between rigid frameworks and flexible linkers. This work expands the known carbon phase space and demonstrates the efficacy of coupling generative AI with machine learning potentials for the accelerated inverse design of functional materials.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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