论文标题
电子结构的多任务学习，以预测和探索分子势能表面
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces
论文作者
论文摘要
我们完善了OrbNet模型，以使用图神经网络结构来准确预测分子的能量，力和其他响应特性，该构建基于低成本近似量子算子的特征，在适应于对称性的原子轨道基础上。由于所有电子结构项的分析梯度导致了分析梯度，因此该模型是端到端微分的，并且由于使用域特异性特征，因此可在化学空间上转移。通过通过多任务学习对电子结构的有力限制纳入了学习效率，可以提高学习效率。该模型的表现优于QM9数据集的能量预测任务的现有方法，以及与传统的量子化学计算（例如密度功能理论）相比，以千倍或更低的计算成本来降低了构型数据集的分子几何优化。
We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy.
