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 C₆₀) 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）计算对于在原子水平上准确解释自然现象是必不可少的。然而，它们惊人的计算成本带来了很大的局限性，幸运的是，通过利用人工智能 (AI) 的进步可以在很大程度上消除这种局限性。这里我们介绍通用的、高度可移植的人工智能——量子力学方法1（AIQM1）。它接近金标准耦合簇 QM 方法的精度，并且具有基态中性闭壳物质的近似低级半经验 QM 方法的高计算速度。 AIQM1 可以为各种有机化合物以及具有挑战性的系统（例如接近实验的大型共轭化合物（富勒烯 C ₆₀ ））提供准确的基态能量。这为以以前无法达到的速度和精度研究化合物提供了机会，正如我们通过确定聚炔分子的几何形状所证明的那样——这项任务对于实验和理论来说都是困难的。值得注意的是，我们的方法对于离子和激发态属性的准确性也很好，尽管 AIQM1 的神经网络部分从未适合这些属性。