Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

The
complex reaction network of catalytic biomass conversions often
involves hundreds of surface intermediates and thousands of reaction
steps, greatly hindering the rational design of metal catalysts for
these conversions. Here, we present a framework of machine learning
(ML)-accelerated first-principles studies for the hydrodeoxygenation
(HDO) of propanoic acid over transition metal surfaces. The microkinetic
model (MKM) is initially parametrized by ML-predicted energies and
iteratively improved by identifying the rate-determining species and
steps (RDS), computing their energies by density functional theory
(DFT), and reparameterizing the MKM until all the RDS are computed
by DFT. The Gaussian process (GP) model performs significantly better
than the linear ridge regression model for predicting both the adsorption
free energies and transition state free energies. Parameterized with
energies from the GP model, only 5–20% of the full reaction
network has to be computed by DFT for the MKM to possess DFT-level
accuracy for the TOF and dominant reaction pathway. While the linear
ridge regression model performs worse than the GP model, its performance
is greatly improved when only transition states are predicted by the
regression model and adsorption energies are computed by DFT. Overall,
we find that a high accuracy in adsorption free energies is more important
for a reliable MKM than a high accuracy in TS free energies. Finally,
based on the GP model with GOH and GCHCHCO as
catalyst descriptors, we build two-dimensional volcano plots in activity
and selectivity that can help design promising alloy catalysts for
HDO reactions of organic acids.

催化生物质转化的复杂反应网络通常包含数百种表面中间体与数千个基元反应步骤，极大阻碍了这类转化所用金属催化剂的理性设计。本研究针对过渡金属表面上丙酸的加氢脱氧（Hydrodeoxygenation，HDO）反应，提出了一套机器学习（Machine Learning，ML）加速的第一性原理研究框架。该微观动力学模型（Microkinetic Model，MKM）最初通过机器学习预测的能量完成参数化，并通过识别决速中间体与决速步（Rate-determining Species and Steps，RDS）、借助密度泛函理论（Density Functional Theory，DFT）计算其能量，随后重新参数化微观动力学模型，直至所有决速步骤均通过密度泛函理论完成计算，以此实现迭代优化。在吸附自由能与过渡态自由能的预测任务中，高斯过程（Gaussian Process，GP）模型的表现显著优于线性岭回归模型。若以高斯过程模型预测的能量进行参数化，仅需通过密度泛函理论计算完整反应网络的5%~20%，即可使微观动力学模型在周转频率（Turnover Frequency，TOF）与优势反应路径方面达到密度泛函理论级别的精度。尽管线性岭回归模型的表现不及高斯过程模型，但当仅通过该回归模型预测过渡态能量、而吸附能通过密度泛函理论计算时，其性能可得到大幅提升。总体而言，本研究发现，相较于过渡态自由能的高精度预测，吸附自由能的高精度预测对构建可靠的微观动力学模型更为关键。最终，基于以GOH与GCHCHCO作为催化剂描述符的高斯过程模型，我们构建了活性与选择性维度的二维火山图，可用于辅助设计有应用前景的合金催化剂，以应用于有机酸的加氢脱氧反应。