标题： 无机晶体生成发现的基线建立 显示英文标题

标题： Establishing baselines for generative discovery of inorganic crystals

摘要： 生成式人工智能为材料发现提供了一条有前景的途径，但其相对于传统方法的优势仍不明确。 在本工作中，我们将两种基线方法——电荷平衡原型的随机枚举和已知化合物的数据驱动离子交换——与四种基于扩散模型、变分自编码器和大语言模型的生成技术进行对比测试。 我们的结果表明，像离子交换这样的成熟方法在生成稳定的新材料方面表现更好，尽管其中许多材料与已知化合物非常相似。 相比之下，生成模型在提出新颖的结构框架方面表现出色，并且在有足够的训练数据时，可以更有效地针对电子带隙和体积模量等性质。 为了提高基线方法和生成方法的性能，我们实施了一个生成后的筛选步骤，其中所有提出的结构都会通过预训练机器学习模型中的稳定性及性质过滤器，包括通用原子间势能模型。 这一低成本的过滤步骤显著提高了所有方法的成功率，计算效率高，并最终为更有效的材料发现生成策略提供了可行的路径。 通过建立比较基准，本研究突显了生成模型持续进步的机会，特别是对于热力学稳定的新型材料的定向生成。 显示英文摘要

摘要： Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and benchmark two baseline approaches - random enumeration of charge-balanced prototypes and data-driven ion exchange of known compounds - against four generative techniques based on diffusion models, variational autoencoders, and large language models. Our results show that established methods such as ion exchange are better at generating novel materials that are stable, although many of these closely resemble known compounds. In contrast, generative models excel at proposing novel structural frameworks and, when sufficient training data is available, can more effectively target properties such as electronic band gap and bulk modulus. To enhance the performance of both the baseline and generative approaches, we implement a post-generation screening step in which all proposed structures are passed through stability and property filters from pre-trained machine learning models including universal interatomic potentials. This low-cost filtering step leads to substantial improvement in the success rates of all methods, remains computationally efficient, and ultimately provides a practical pathway toward more effective generative strategies for materials discovery. By establishing baselines for comparison, this work highlights opportunities for continued advancement of generative models, especially for the targeted generation of novel materials that are thermodynamically stable.