准原子最小基表象分子波函数的深度神经网络
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
摘要：
量子化学中机器学习方法的出现为重新审视一个老问题提供了新方法：电子结构计算的预测准确性是否可以与其数值瓶颈脱钩？以前试图回答这个问题，在其他方法中，产生了最小基表示的半经验量子化学。我们提出了最近提出的SchNet for Orbitals（SchNOrb）深度卷积神经网络模型的适应[Nature Commun. 10，5024（2019）]的电子波函数在一个优化的准原子最小基表示。对于5个有机分子，范围从5到13个重原子，该模型准确地预测分子轨道能量和波函数，并提供了获得化学键分析的衍生性质。特别是对于较大的分子，该模型优于原来的原子轨道为基础的SchNOrb方法在精度和缩放。最后，我们讨论了这种方法在量子化学工作流程中的未来潜力。
英文摘要：
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.
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