发表机构
BAM（联邦材料研究与测试研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出利用预训练MLIPs（如MACE）的原子平均特征，引入CFTD距离度量评估材料生成模型，同时展示其可作为模型指导，实现晶体结构质量评估与记忆检测。
AI 中文摘要
生成式机器学习越来越多地用于无机晶体结构生成，大多数模型及对应的评估方法依赖简单形式的晶体结构表示。本文展示了预训练机器学习原子间势（MLIPs，如MACE）的原子平均特征在这类任务中的作用。我们首先引入一种距离度量，通过单一基于分布的评估框架同时捕捉材料生成模型输出的质量与新颖性，具体而言，我们使用两种不同的特征化器引入粗精传输距离（CFTD），其中质量分量基于粗MACE特征。我们展示了CFTD在捕捉晶体结构质量、检测记忆现象方面的通用性，并将其与近期提出的连续SUN指标进行比较。我们还进一步表明，粗MACE特征可作为材料生成模型的指导。
英文摘要
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.
