论文标题
用均衡的机器学习模型预测张力分子特性
Predicting tensorial molecular properties with equivariant machine-learning models
论文作者
论文摘要
将分子对称性嵌入机器学习模型是有效学习化学物理标量特性的关键，但是几乎没有证据表明如何将相同的策略扩展到张力量。在这里，我们根据局部原子环境描述符制定可伸缩的均等机器学习模型。我们将其应用于一系列分子，并表明可以对不同等级的介电和磁性张力特性的全面列表实现准确的预测。这些结果表明，模型模型是扩展材料建模中机器学习范围的有前途的平台。
Embedding molecular symmetries into machine-learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a scalable equivariant machine-learning model based on local atomic environment descriptors. We apply it to a series of molecules and show that accurate predictions can be achieved for a comprehensive list of dielectric and magnetic tensorial properties of different ranks. These results show that equivariant models are a promising platform to extend the scope of machine learning in materials modelling.
