State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Nat Commun. 2021 Dec 2;12(1):7022. doi: 10.1038/s41467-021-27340-2.
High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence-quantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C) close to experiment. This opens an opportunity to investigate chemical compounds with previously unattainable speed and accuracy as we demonstrate by determining geometries of polyyne molecules-the task difficult for both experiment and theory. Noteworthy, our method's accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties.
高水平的量子力学（QM）计算对于准确解释原子水平的自然现象是不可或缺的。然而，其计算成本之高带来了巨大的限制，幸运的是，通过利用人工智能（AI）的进步，可以在很大程度上缓解这些限制。在这里，我们引入了通用的、高度可转移的人工智能-量子力学方法 1（AIQM1）。对于中性、闭壳层的基态物质，它以近似低级半经验 QM 方法的高计算速度接近金标准耦合簇 QM 方法的准确性。AIQM1 可以为各种有机化合物提供准确的基态能量，甚至可以为具有挑战性的系统（如接近实验的大共轭化合物（富勒烯 C））提供准确的几何形状。这为我们提供了一个机会，以以前无法达到的速度和准确性来研究化学化合物，正如我们通过确定多炔分子的几何形状所证明的那样，这对于实验和理论来说都是一项困难的任务。值得注意的是，尽管 AIQM1 的神经网络部分从未针对这些性质进行过拟合，但我们的方法在离子和激发态性质方面的准确性也很好。
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