Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex.
Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex.
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DOI：
10.1039/d0sc00445f
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发表时间：
2020-05-14
期刊：
影响因子：
8.4
通讯作者：
Balcells D
中科院分区：
文献类型：
--
作者：
Friederich P;Dos Passos Gomes G;De Bin R;Aspuru-Guzik A;Balcells D
对瓦斯卡综合体周围化学空间的机器学习探索。使用过渡金属络合物的均相催化广泛用于有机合成，并且在水分解和二氧化碳还原等应用中具有技术相关性。均相催化的关键步骤需要与金属中心结合的配体的电子效应和空间效应的特定组合。由于存在大量的可能性和非平凡的配体-配体相互作用，寻找配体的最佳组合是一项具有挑战性的任务。 Vaska 配合物反式-[Ir(PPh3)2(CO)(Cl)] 的经典例子说明了这种情况。该物质的配体激活铱以氧化加成氢，产生二氢化物顺式-[Ir(H)2(PPh3)2(CO)(Cl)]络合物。尽管该系统很简单，但可以配制数千种衍生物来激活 H2，其中有限数量的配体属于原始复合物中发现的相同一般类别。在这项工作中，我们展示了如何将 DFT 和机器学习 (ML) 方法结合起来，以预测包含数千个复合物的大型化学空间内的反应性。在 Vaska 复合体衍生的 2574 个物种的空间中，DFT 计算的数据用于训练和测试预测 H2 激活势垒的 ML 模型。与需要几天才能完成的实验和计算相比，机器学习模型在笔记本电脑上的训练和使用时间为几分钟。作为第一种方法，我们将贝叶斯优化的人工神经网络 (ANN) 与自相关和增量函数导出的特征相结合。由此产生的 ANN 实现了高精度，平均绝对误差 (MAE) 在 1 到 2 kcal mol–1 之间，具体取决于训练集的大小。通过使用使用一组选定特征（包括指纹）进行训练的高斯过程 (GP) 模型，准确性进一步提高。值得注意的是，该 GP 模型仅使用 20% 或更少的可用于训练的数据，将 MAE 降至 1 kcal mol–1 以下。梯度提升（GB）方法还用于评估特征的相关性，用于特征选择和模型解释目的。研究发现，化学成分、原子大小和电负性等特征是预测中最具决定性的因素。此外，还鉴定了对 H2 激活屏障影响最强的配体片段。
A machine learning exploration of the chemical space surrounding Vaska's complex. Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO2 reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh3)2(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)2(PPh3)2(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H2, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H2-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol–1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol–1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H2-activation barrier were identified.
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影响因子：
62.1
作者：
Álvarez A;Bansode A;Urakawa A;Bavykina AV;Wezendonk TA;Makkee M;Gascon J;Kapteijn F
通讯作者：
Kapteijn F
影响因子：
13.6
作者：
Bartók AP;De S;Poelking C;Bernstein N;Kermode JR;Csányi G;Ceriotti M
通讯作者：
Ceriotti M
影响因子：
--
作者：
Baumes, L. A.;Serra, J. M.;Corma, A.
通讯作者：
Corma, A.
DOI：
10.1021/ci9803381
发表时间：
1999-07-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Butina, D
通讯作者：
Butina, D
影响因子：
4.4
作者：
Behler, Joerg
通讯作者：
Behler, Joerg