Exploration of nonlinear parallel heterogeneous reaction pathways through Bayesian variable selection
Exploration of nonlinear parallel heterogeneous reaction pathways through Bayesian variable selection
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DOI：
10.1140/epjb/s10051-021-00053-7
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发表时间：
2021-02-02
影响因子：
1.6
通讯作者：
Omori, Toshiaki
中科院分区：
文献类型：
--
作者：
Oyanagi, Ryosuke X.;Kuwatani, Tatsu;Omori, Toshiaki
反演是提取自然科学中并行发生的非均相反应控制的非线性动力学的关键方法。因此，在本研究中，我们提出了一个贝叶斯统计框架，仅使用固相的噪声可观察空间分布来确定活性反应途径。在该方法中，使用广泛适用的贝叶斯信息准则（WBIC）探索主动反应途径，该准则用于在贝叶斯推理框架内选择模型。通过最大化后验分布来确定合理的反应机制。该条件概率是通过马尔可夫链蒙特卡罗模拟获得的。然后使用固相的模拟空间数据确定所提出方法的效率。结果表明，可以从反应路径的冗余候选中识别出活跃的反应路径。排除这些多余的反应途径后，可以高精度地估计反应动力学的控制因素。
Inversion is a key method for extracting nonlinear dynamics governed by heterogeneous reaction that occur in parallel in the natural sciences. Therefore, in this study, we propose a Bayesian statistical framework to determine the active reaction pathways using only the noisy observable spatial distribution of the solid phase. In this method, active reaction pathways were explored using a Widely Applicable Bayesian Information Criterion (WBIC), which is used to select models within the framework of Bayesian inference. Plausible reaction mechanisms were determined by maximizing the posterior distribution. This conditional probability is obtained through Markov chain Monte Carlo simulations. The efficiency of the proposed method is then determined using simulated spatial data of the solid phase. The results show that active reaction pathways can be identified from the redundant candidates of reaction pathways. After these redundant reaction pathways were excluded, the controlling factor of the reaction dynamics was estimated with high accuracy.