Department of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford University, South Parks Road, Oxford OX1 3QZ, Great Britain.
MHP Management- und IT-Beratung GmbH, Königsallee 49, Ludwigsburg 71638, Germany.
Anal Chem. 2021 Oct 5;93(39):13360-13372. doi: 10.1021/acs.analchem.1c03154. Epub 2021 Sep 23.
Artificial intelligence (AI) is used to learn the key voltammetric characteristics of the dissociative CE mechanism via training from multiple simulations using bespoke code. This allows first for the prediction of voltammograms without the need for further simulations, given knowledge of the relevant experimental parameters (rate and equilibrium constants, electrode geometry, and diffusion coefficients). Second, it is applied to analyze noisy experimental voltammetry to characterize the mechanistic type and to successfully extract the key kinetic and thermodynamic parameters.
人工智能(AI)被用于通过使用定制代码从多个模拟中进行训练来学习解离 CE 机制的关键伏安特性。这使得在已知相关实验参数(速率和平衡常数、电极几何形状和扩散系数)的情况下,可以预测伏安图,而无需进一步的模拟。其次,它被应用于分析有噪声的实验伏安法以表征机理类型,并成功提取关键的动力学和热力学参数。