对比多种对称性模型，发现排列对称嵌入最适于分子几何学习。
PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning
- 采用排列与旋转对称性设计量子机器学习模型
- 排列对称嵌入在两种分子上均表现最佳，泛化性最强
- 图嵌入提升训练稳定性，适合几何数据建模
本文在分子几何层次结构下比较了几何量子机器学习模型的性能。研究对象为线性构型的LiH分子和三角锥形的NH3分子。同时评估了准确率与泛化能力。以经典等变模型为基线，对比了无对称性、旋转与排列等变、以及图嵌入排列等变的量子模型性能。结果表明，模型性能差异与分子几何密切相关，揭示了选择模型的依据。图嵌入特征被证明是提升几何数据可训练性的有效途径。排列对称嵌入被发现是最具泛化能力的量子机器学习模型。
原文摘要 · Abstract (English)
In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyramidal molecule NH3. Both accuracy and generalizability metrics are considered. A classical equivariant model is used as a baseline for the performance comparison. The comparative performance of Quantum Machine Learning models with no symmetry equivariance, rotational and permutational equivariance, and graph embedded permutational equivariance is investigated. The performance differentials and the molecular geometry in question reveals the criteria for choice of models for generalizability. Graph embedding of features is shown to be an effective pathway to greater trainability for geometric datasets. Permutational symmetric embedding is found to be the most generalizable quantum Machine Learning model for geometric learning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。