发表机构
École Polytechnique Fédérale de Lausanne; Institut des Matériaux; Laboratory of Computational Science and Modeling（洛桑联邦理工学院; 材料研究所; 计算科学与建模实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究借助大语言模型发现原子中心结构描述符在考虑至多七个邻原子簇时仍存在简并，揭示了AI在跨领域转化成果、加速科学突破的新应用模式。
AI 中文摘要
将原子结构映射为一组紧凑的几何描述符是机器学习应用于原子尺度建模的关键步骤。一种强大且广泛使用的方法可理解为对两两距离、三角形等的直方图进行离散化，从而形成具有对称性不变性的原子中心描述符层级。遗憾的是，该层级的较低层级（二、三、四邻原子簇）被发现存在不完备性，即对称性无关的结构对具有完全相同的描述符。不过，迄今为止报道的所有“描述符简并”均可通过考虑更大的邻原子簇来构建描述符加以解决。我们借助大语言模型发现了3D结构的实例，即使考虑多达七个邻原子簇，这些结构仍无法区分，且在描述符的实际离散化水平下可达到任意阶。其构建的关键要素可追溯到不同领域数十年前已知的结果；该模型能够找到相关文献并认识到其对当前问题的重要性。我们认为，该实验揭示了AI在科学领域的一种极具成效的应用模式：在不同领域和应用之间转化研究成果，加速某一领域的偶然发现转变为另一领域范式级突破的进程。
英文摘要
Mapping an atomic structure to a compact set of geometric descriptors is an essential step in any machine-learning application to atomic-scale modeling. A powerful and widely-used approach can be understood as a discretization of the histogram of pair distances, triangles, etc., that results in a hierarchy of symmetry-invariant atom-centered descriptors. Unfortunately, the lower rungs on this hierarchy (two, three, four-neighbor clusters) were found to be incomplete, with symmetry-unrelated pairs of structures having exactly the same descriptors. However, all the ``degeneracies'' reported so far are resolved by considering larger clusters of neighbors to build the descriptors. We report examples of 3D structures that are indistinguishable even if one considers clusters of up to seven neighbors, and to arbitrary order when considering a practical level of discretization of the descriptors, discovered with the assistance of large language models. The key ingredients in their construction can be traced to results that have been known for decades in different communities: the model was able to find the references and recognize their significance for the problem at hand. We believe this experiment exposes an extremely fruitful usage pattern for AI in science: translating results between different communities and application domains, accelerating the process by which serendipitous discoveries in a field become breakthroughs in another.
