Chen Yuxinxin, Dral Pavlo O
State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemistry, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University Xiamen 361005 China
Aitomistic Shenzhen 518000 China.
Chem Sci. 2025 Aug 7. doi: 10.1039/d5sc02802g.
Density functional theory (DFT) is the workhorse of reaction simulations, but it often suffers from either prohibitive cost or insufficient accuracy. In this work, we report AIQM2-the universal AI-enhanced QM method 2-as the first method that enables fast and accurate large-scale organic reaction simulations for practically relevant system sizes and time scales beyond what is possible with DFT. This breakthrough is based on the high speed of AIQM2, which is orders of magnitude faster than common DFT, while its accuracy in reaction energies, transition state optimizations, and barrier heights is at least at the level of DFT and often approaches the gold-standard coupled cluster accuracy. AIQM2 can be used out of the box without any further retraining. Compared to pure machine learning potentials, AIQM2 possesses high transferability and robustness in simulations without catastrophic breakdowns. We showcase the superiority of AIQM2 compared to traditional DFT by performing an extensive reaction dynamics study overnight and revising the mechanism and product distribution reported in the previous investigation of the bifurcating pericyclic reaction.
密度泛函理论（DFT）是反应模拟的主力方法，但它常常面临成本过高或精度不足的问题。在这项工作中，我们报告了AIQM2——通用人工智能增强量子力学方法2——作为第一种能够对实际相关的系统规模和时间尺度进行快速且准确的大规模有机反应模拟的方法，这超越了DFT所能达到的范围。这一突破基于AIQM2的高速度，它比普通DFT快几个数量级，而其在反应能量、过渡态优化和势垒高度方面的精度至少与DFT相当，并且常常接近金标准耦合簇精度。AIQM2开箱即用，无需任何进一步的重新训练。与纯机器学习势相比，AIQM2在模拟中具有高转移性和鲁棒性，不会出现灾难性崩溃。我们通过进行一项耗时一夜的广泛反应动力学研究，并修正先前对分叉周环反应研究中报道的机理和产物分布，展示了AIQM2相对于传统DFT的优越性。
