用预训练势能模型特征做材料生成的粗粒度坐标，提升生成质量与新颖性评估。
Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation
- 用MACE等预训练势能模型提取原子平均特征作为粗粒度坐标
- 提出CFTD评估框架，同时衡量生成结构的质量与新颖性
- 该特征可指导生成模型，适合材料生成与评估研究者使用
生成式机器学习在无机晶体结构生成中日益普及。现有模型与评估方法多依赖简单的晶体结构表示。本文展示基于预训练机器学习原子间势能（MLIPs）如MACE的原子平均特征在该任务中的强大能力。我们提出一种基于分布的评估框架——粗-细运输距离（CFTD），通过两种不同特征提取器实现，其中质量评估部分基于粗粒度的MACE特征。CFTD可有效捕捉晶体结构质量并检测记忆现象，且与近期提出的连续SUN指标进行对比。进一步表明，粗粒度的MACE特征可作为生成模型的引导信号。
原文摘要 · Abstract (English)
Generative machine learning is increasingly used for inorganic crystal structure generation. Most models and the corresponding evaluation approaches rely on simple forms of crystal structure representation. In this paper, we showcase the power of atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs), such as MACE, for such tasks. We first introduce a distance measure that assesses the output of material generative models by capturing both quality and novelty in a single distribution-based evaluation framework. In particular, we introduce the Coarse-Fine Transport Distance (CFTD) using two different featurizers, where the quality component is based on coarse MACE features. We showcase CFTD's versatility in capturing crystal-structure quality while also detecting memorization, and compare it with the recently introduced continuous SUN metrics. We further show that coarse MACE features can be used as guidance for a material generative model.
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