[J. Phys. Chem. Lett.] 我室Pavlo O. Dral教授发表论文:Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

发布日期:2022年04月25日   浏览次数:

我室Pavlo O. Dral教授在 J. Phys. Chem. Lett. 上发表论文:Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods

摘要:Enthalpies of formation and reaction are important thermodynamic properties that have a crucial impact on the outcome of chemical transformations. Here we implement the calculation of enthalpies of formation with a general-purpose ANI-1ccx neural network atomistic potential. We demonstrate on a wide range of benchmark sets that both ANI-1ccx and our other general-purpose data-driven method AIQM1 approach the coveted chemical accuracy of 1 kcal/mol with the speed of semiempirical quantum mechanical methods (AIQM1) or faster (ANI-1ccx). It is remarkably achieved without specifically training the machine learning parts of ANI-1ccx or AIQM1 on formation enthalpies. Importantly, we show that these data-driven methods provide statistical means for uncertainty quantification of their predictions, which we use to detect and eliminate outliers and revise reference experimental data. Uncertainty quantification may also help in the systematic improvement of such data-driven methods.

文章链接:https://pubs.acs.org/doi/10.1021/acs.jpclett.2c00734