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Predicting the performance of oxidation catalysts using descriptor models

机译：预测氧化催化剂的性能使用描述符模型

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摘要

Practical solutions in catalysis require catalysts that are active and stable. Mixed metal oxides are robust materials, and as such are often used as industrial catalysts. The problem is that predicting their performance a priori is difficult. Following our work on simple descriptors for supported metals based on Slater-type orbitals, we show here that a similar paradigm holds also for metal oxides. Using the oxidative dehydrogenation of butane to 1,3-butadiene as a model reaction, we synthesised and tested 15 bimetallic mixed oxides supported on alumina. We then built a descriptor model for these oxides, and projected the model's results on a set of 1711 mixed oxide catalysts in silico. Based on the model's predictions, six new bimetallic oxides were then synthesised and tested. Experimental validation showed impressive results, with Q(2) > 0.9, demonstrating the power of these low-cost predictive models. Importantly, no interaction terms were included in the model, showing that even if we think that bimetallic oxide catalysts are highly complex materials, their performance can be predicted using simplistic models. The implications of these findings to catalyst optimisation practices in academia and industry are discussed.
机译：实际的解决方案需要催化剂催化积极和稳定。健壮的材料,因此经常使用作为工业催化剂。预测他们的表现是先天的困难。基于描述符支持金属Slater-type轨道,我们展示了一个类似的模式同样适用于金属氧化物。丁烷氧化脱氢的反应1,三丁基作为模型反应,我们合成15双金属氧化物混合和测试支持在氧化铝。这些氧化物,预测模型的结果在1711年的一组混合氧化物催化剂在硅。基于模型的预测,6个新双金属氧化物被合成测试。结果,与Q(2) > 0.9,展示了权力这些低成本的预测模型。没有交互条款包含在模型中,显示,即使我们认为双金属氧化催化剂是高度复杂的材料,他们可以预测使用的表现简单的模型。发现催化剂优化实践学术界和产业界进行了讨论。

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