标题： 基于等变图神经网络和相关性分类的自旋交叉候选物发现 显示英文标题

标题： Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification

摘要： Swift discovery of spin-crossover materials for their potential application in quantum information devices requires techniques which enable efficient identification of suitably bistable candidates. 快速发现用于量子信息器件潜在应用的自旋交叉材料需要能够有效识别合适双稳态候选材料的技术。 To this end, we screened the Cambridge Structural Database to develop a specialized database of 1,439 materials and computed spin-switching energies from density functional theory for each material. 为此，我们筛选了剑桥结构数据库，开发了一个包含1,439种材料的专业数据库，并为每种材料计算了从密度泛函理论得出的自旋转换能量。 The database was used to train an equivariant graph convolutional neural network to predict the magnitude of the spin-conversion energy. 该数据库被用来训练一个等变图卷积神经网络，以预测自旋转换能量的大小。 A test mean absolute error was 360 meV. 测试的平均绝对误差为360 meV。 For candidate identification, we equipped the system with a relevance-based classifier. 为了候选材料的识别，我们为系统配备了基于相关性的分类器。 This approach leads to a nearly four-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening. 与传统高通量筛选相比，这种方法在识别感兴趣的潜在自旋交叉系统方面几乎提高了四倍的效率。 显示英文摘要

摘要： Swift discovery of spin-crossover materials for their potential application in quantum information devices requires techniques which enable efficient identification of suitably bistable candidates. To this end, we screened the Cambridge Structural Database to develop a specialized database of 1,439 materials and computed spin-switching energies from density functional theory for each material. The database was used to train an equivariant graph convolutional neural network to predict the magnitude of the spin-conversion energy. A test mean absolute error was 360 meV. For candidate identification, we equipped the system with a relevance-based classifier. This approach leads to a nearly four-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening.