AI 中文总结
研究针对晶体材料逆设计中属性引导相关问题，基于DiffCrysGen开发框架，用参数高效适配器微调及无分类器引导，以多种属性为任务研究其影响，经预筛选和验证识别出稳定材料，确立该框架并提供新见解。
AI 中文摘要
基于属性引导的扩散生成模型已成为逆材料设计的一个有前景的范例，可生成具有用户指定目标属性的晶体材料。然而，尽管有进展，但属性引导的有效性、其对晶体对称性的影响以及生成材料的物理可行性仍了解不足。为解决这些问题，我们基于轻量级扩散模型DiffCrysGen开发了一个属性引导框架，使用参数高效的适配器微调及无分类器引导（CFG）。以形成能、饱和磁化强度和维氏硬度为代表性逆设计任务，系统研究了不同引导强度下CFG的影响。增加引导尺度可使生成属性分布趋向规定目标，减少最低对称性（$P1$）结构比例，增加高对称性结构比例。通过基于机器学习原子间势（MLIP）的工作流程对生成结构进行几何预筛选和验证，该框架分别以12.3%和3.9%的总体成功率识别出热力学和动力学稳定的磁性及机械硬质材料。这些结果确立了属性引导的DiffCrysGen作为逆材料设计的有效框架，并为无分类器引导在晶体生成中的作用提供了新见解。
英文摘要
Diffusion-based generative models with property guidance have emerged as a promising paradigm for inverse materials design by enabling the generation of crystalline materials with user-specified target properties. However, despite recent advances, the effectiveness of property guidance, its influence on crystallographic symmetry, and the physical viability of generated materials remain poorly understood. To address these questions, we develop a property-guided framework based on the lightweight diffusion model DiffCrysGen using parameter-efficient adapter fine-tuning and classifier-free guidance (CFG). The resulting framework enables efficient multi-property crystal generation while preserving the knowledge learned during unconditional pre-training. Using formation energy together with saturation magnetization and Vickers hardness as representative inverse-design tasks, we systematically investigate the influence of CFG across a broad range of guidance strengths. Increasing the guidance scale progressively steers the generated property distributions toward the prescribed targets while reducing the fraction of lowest-symmetry ($P1$) structures and increasing the proportion of higher-symmetry structures. To evaluate physical viability, generated structures are geometrically prescreened and subsequently validated using a machine-learning interatomic potential (MLIP)-based workflow comprising structural relaxation and thermodynamic, dynamical, and property-specific analyses. The framework identifies thermodynamically and dynamically stable magnetic and mechanically hard materials with overall success rates of 12.3\% and 3.9\%, respectively. These results establish property-guided DiffCrysGen as an efficient framework for inverse materials design while providing new insights into the role of classifier-free guidance in crystal generation.
Comments15 pages, 12 figures
