论文标题
以准原子最小值表示分子波函数的深神网络
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
论文作者
论文摘要
量子化学中机器学习方法的出现提供了重新审视旧问题的新方法：电子结构计算的预测准确性是否可以与其数值瓶颈解耦？先前试图回答这个问题的尝试除其他方法外，还以最小的基础表示产生了半经验量子化学。我们提出了最近提出的用于轨道（Schnorb）深卷积神经网络模型[Nature Commun。 10，5024（2019）]用于优化的准原子最小值表示电子波函数。对于5到13个重原子不等的五个有机分子，该模型准确地预测了分子轨道能和波函数，并为化学键合分析提供了访问衍生的特性。特别是对于较大的分子，该模型就准确性和缩放而言优于原始原子 - 轨道方法。我们通过讨论这种方法在量子化学工作流程中的未来潜力来结束。
The emergence of machine learning methods in quantum chemistry provides new methods to revisit an old problem: Can the predictive accuracy of electronic structure calculations be decoupled from their numerical bottlenecks? Previous attempts to answer this question have, among other methods, given rise to semi-empirical quantum chemistry in minimal basis representation. We present an adaptation of the recently proposed SchNet for Orbitals (SchNOrb) deep convolutional neural network model [Nature Commun. 10, 5024 (2019)] for electronic wave functions in an optimised quasi-atomic minimal basis representation. For five organic molecules ranging from 5 to 13 heavy atoms, the model accurately predicts molecular orbital energies and wavefunctions and provides access to derived properties for chemical bonding analysis. Particularly for larger molecules, the model outperforms the original atomic-orbital-based SchNOrb method in terms of accuracy and scaling. We conclude by discussing the future potential of this approach in quantum chemical workflows.
