Departamento de Química - ICEx, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil.
Departamento de Química, CEFET, Contagem, Minas Gerais, Brazil.
J Mol Model. 2022 Sep 2;28(9):286. doi: 10.1007/s00894-022-05274-w.
The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of potential energy function, [Formula: see text], between adsorbate and adsorbent from experimental data. Since the adsorption data is related to the second virial coefficient and therefore to [Formula: see text] through an integral equation, the Hopfield Neural Network will be used to find the best parameters which fits the experimental data. Initially simulated results will be analyzed to verify the method performance for data sets with and without noise addition. Then, experimental data for adsorption of propionitrile on activated carbon will be treated. Results presented here corroborate to the robustness of this method.
Hopfield 神经网络已成功应用于解决化学和物理学不同领域的不适定反问题。在这项工作中，该方法的非线性方法将用于从实验数据中检索势能函数经验参数 [Formula: see text]，其中吸附质和吸附剂之间。由于吸附数据与第二维里系数有关，因此通过积分方程与 [Formula: see text] 有关，因此将使用 Hopfield 神经网络来找到最适合实验数据的最佳参数。最初的模拟结果将进行分析，以验证该方法对有噪声和无噪声添加数据集的性能。然后，将处理丙烯腈在活性炭上吸附的实验数据。这里呈现的结果证实了该方法的稳健性。
