利用动力学信息神经网络对产品数据的时间分析进行微观动力学建模†。

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-10-10 DOI:10.1039/D4DD00163J
Dingqi Nai, Gabriel S. Gusmão, Zachary A. Kilwein, Fani Boukouvala and Andrew J. Medford
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引用次数: 0

摘要

产品的时间分析(TAP)技术会产生大量的瞬态动力学数据集,但要将大量原始数据转化为物理上可解释的动力学模型却很困难,这主要是由于现有拟合 TAP 数据的数值方法的计算规模所致。在这项工作中,我们利用动力学信息神经网络(KINNs)来建立 TAP 数据模型,KINNs 是一种人工前馈神经网络,旨在求解受微观动力学模型约束的常微分方程。我们证明,在已知薄催化剂区所有浓度的假设下,KINNs 可以同时拟合瞬态数据、检索动力学模型参数,并对多脉冲实验中未见的脉冲行为进行插值。我们进一步证明,通过修改损失函数,KINNs 即使在无法获得精确的薄区信息(如真实的 TAP 实验数据)的情况下也能保持这些能力。我们还将该方法与现有的优化技术进行了比较,结果表明,该方法在提取动力学参数方面具有更好的噪音容忍度和性能。KINNs 方法为 TAP 分析提供了一种有效的替代方法,有助于解释复杂系统的长时间尺度瞬态动力学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Micro-kinetic modeling of temporal analysis of products data using kinetics-informed neural networks†

The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically interpretable kinetic models, largely due to the computational scaling of existing numerical methods for fitting TAP data. In this work, we utilize kinetics-informed neural networks (KINNs), which are artificial feedforward neural networks designed to solve ordinary differential equations constrained by micro-kinetic models, to model the TAP data. We demonstrate that, under the assumption that all concentrations are known in the thin catalyst zone, KINNs can simultaneously fit the transient data, retrieve the kinetic model parameters, and interpolate unseen pulse behavior for multi-pulse experiments. We further demonstrate that, by modifying the loss function, KINNs maintain these capabilities even when precise thin-zone information is unavailable, as would be the case with real experimental TAP data. We also compare the approach to existing optimization techniques, which reveals improved noise tolerance and performance in extracting kinetic parameters. The KINNs approach offers an efficient alternative for TAP analysis and can assist in interpreting transient kinetics in complex systems over long timescales.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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