LeMat-GenBench：一种统一的晶体生成模型评估框架
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
AI总结：
LeMat-GenBench通过统一的评估框架和指标，评估晶体生成模型的稳定性与多样性，揭示稳定性与新颖性之间的权衡，为生成模型的发展提供指导。
摘要：
生成机器学习（ML）模型在通过无机晶体的反向设计加速材料发现方面具有巨大潜力，能够前所未有的探索化学空间。然而，缺乏标准化的评估框架使得难以评估、比较和进一步发展这些ML模型。在本工作中，我们引入了LeMat-GenBench，一种统一的晶体生成模型评估基准，配套了一系列评估指标，旨在更好地指导模型开发和后续应用。我们发布了开源评估套件和公开排行榜板，并对12种最近的生成模型进行了基准测试。结果表明，稳定性增加平均导致新颖性和多样性降低，没有模型在所有维度上表现出色。总体而言，LeMat-GenBench建立了一个可重复和可扩展的基础，为公平的模型比较提供了基础，并旨在指导开发更可靠、面向发现的晶体生成模型。
英文摘要：
Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented exploration of chemical space. Yet, the lack of standardized evaluation frameworks makes it challenging to evaluate, compare, and further develop these ML models meaningfully. In this work, we introduce LeMat-GenBench, a unified benchmark for generative models of crystalline materials, supported by a set of evaluation metrics designed to better inform model development and downstream applications. We release both an open-source evaluation suite and a public leaderboard on Hugging Face, and benchmark 12 recent generative models. Results reveal that an increase in stability is accompanied by lower novelty and diversity on average, with no model excelling across all dimensions. Altogether, LeMat-GenBench establishes a reproducible and extensible foundation for fair model comparison and aims to guide the development of more reliable, discovery-oriented generative models for crystalline 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。
