三篇新研究论文介绍了加速材料发现的先进人工智能技术。UNATE 利用无监督原子嵌入来改进晶体性质预测，在数据有限的情况下显示出显著的收益。Crys-JEPA 开发了晶体的能量感知潜在空间，能够更有效地筛选和优化生成的材料。Composable Crystals 引入了一个基于概念的框架，用于可控的材料发现，能够引导生成新颖且稳定的晶体结构。 AI
影响 这些人工智能驱动的材料发现方面的进步可以显著加快具有所需特性的新材料的开发速度。
排序理由 该集群包含三篇学术论文，详细介绍了材料科学研究的新型人工智能方法。
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arXiv:2605.25866v1 Announce Type: new Abstract: Accurately predicting crystal properties is critical for accelerating materials discovery, but it is often limited by scarce labeled data and costly theoretical calculations. To alleviate this, we propose UNATE (Unsupervised Atomic …
arXiv:2605.14759v2 Announce Type: replace Abstract: De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize the likelihood of observed crystals, which encoura…
arXiv:2605.14769v2 Announce Type: replace Abstract: De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. Existing methods mainly rely on black-box stochastic sampling, providing limi…
Accurately predicting crystal properties is critical for accelerating materials discovery, but it is often limited by scarce labeled data and costly theoretical calculations. To alleviate this, we propose UNATE (Unsupervised Atomic Embedding), a framework that leverages structura…