用扩散模型快速生成非晶材料结构，速度比传统方法快1000倍。
A Generative Diffusion Model for Amorphous Materials
- 基于扩散模型生成非晶结构，支持不同成分和工艺条件。
- 生成的二氧化硅结构与真实样品在短程和中程有序上一致。
- 可生成大尺度结构并发现脆性转变，适合材料设计与仿真研究。
生成模型在分子和无机晶体逆向设计中表现优异，但在更复杂的非晶材料中仍难奏效。本文提出一种扩散模型，可在不同制备条件、成分和数据源下，将非晶结构生成速度提升至传统模拟的1000倍。生成的结构在短程与中程有序性、采样多样性及宏观性质上均与二氧化硅玻璃一致，经模拟和信息论策略验证。通过条件生成，在10⁻² K/ps的低冷却速率下成功构建大尺寸结构，揭示了从延性到脆性的转变，并发现了介孔二氧化硅结构。该方法扩展至金属玻璃体系，能准确复现计算与实验数据中的局部结构与性质，证明了从表征结果生成合成数据的可行性。本方法为以往难以实现的非晶材料设计与仿真提供了新路径。
原文摘要 · Abstract (English)
Generative models show great promise for the inverse design of molecules and inorganic crystals, but remain largely ineffective within more complex structures such as amorphous materials. Here, we present a diffusion model that reliably generates amorphous structures up to 1000 times faster than conventional simulations across processing conditions, compositions, and data sources. Generated structures recovered the short- and medium-range order, sampling diversity, and macroscopic properties of silica glass, as validated by simulations and an information-theoretical strategy. Conditional generation allowed sampling large structures at low cooling rates of 10$^{-2}$ K/ps to uncover a ductile-to-brittle transition and mesoporous silica structures. Extension to metallic glassy systems accurately reproduced local structures and properties from both computational and experimental datasets, demonstrating how synthetic data can be generated from characterization results. Our methods provide a roadmap for the design and simulation of amorphous materials previously inaccessible to computational methods.
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