密度泛函理论
计算机科学
替代模型
高斯过程
缩放比例
高斯分布
反应速率
反应机理
统计物理学
化学
计算化学
催化作用
机器学习
数学
物理
生物化学
几何学
作者
Zachary W. Ulissi,Andrew J. Medford,Thomas Bligaard,Jens K. Nørskov
摘要
Abstract Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.
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