Machine learning meets volcano plots: computational discovery of cross-coupling catalysts†,Chemical Science

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Machine learning meets volcano plots: computational discovery of cross-coupling catalysts†
Chemical Science ( IF 7.6 ) Pub Date : 2018-07-13 00:00:00 , DOI: 10.1039/c8sc01949e
Benjamin Meyer _{1,

2} , Boodsarin Sawatlon _{1,

2} , Stefan Heinen _{2,

3} , O Anatole von Lilienfeld _{2,

3} , Clémence Corminboeuf _{1,

2}

Affiliation

The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C–C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.

中文翻译：

机器学习遇到火山图：交叉耦合催化剂的计算发现†

现代机器学习在原子模拟挑战中的应用越来越受到关注。我们提出了新的机器学习模型，可以预测过渡金属络合物和 C-C 交叉偶联反应底物之间氧化加成过程的能量。反过来，这个量可以用作描述符，以使用分子火山图来估计均相催化剂的活性。这种方法的多功能性在基于 Pt、Pd、Ni、Cu、Ag 和 Au 以及 91 种配体的大量有机金属催化剂中得以体现。对总共 18,062 种化合物进行了样本外机器学习预测，导致 557 种候选催化剂落入理想热力学窗口。通过寻找估计价格低于每毫摩尔 10 美元的候选药物，进一步细化了这一数字。入围的 37 种催化剂以钯膦配体组合为主，但也包括地球上丰富的过渡金属 (Cu) 和不太常见的配体。我们的结果表明，现代统计学习技术可以应用于计算发现容易获得且有前途的候选催化剂。

更新日期：2018-07-13

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