使用 Kinetica.jl 预测可变实验条件下的长时间尺度动力学
Predicting long timescale kinetics under variable experimental conditions with Kinetica.jl
AI总结：
针对长时间尺度分子降解预测的计算挑战，提出 Kinetica.jl 软件包，通过动力学驱动算法自动构建大规模化学反应网络并结合机器学习模型，实现了复杂化学系统在可变实验条件下的高效长时间尺度动力学建模。
摘要：
预测分子在长时间尺度上的降解过程是工业材料设计的关键方面。然而，构建与实验条件相关的大型化学反应网络（动力学模型必须反映这些条件）使其在计算上具有挑战性，且每个反应都需要准确的动力学数据。在此，我们展示 Kinetica.jl，这是一个通过用动力学驱动算法探索化学反应空间，以全自动方式构建大规模化学反应网络的新软件包；结合采样基元反应活化能的高效机器学习模型，我们展示了这种方法如何轻松实现包含约 $10^{3}$ 种化学物质和 $10^{4}$ - $10^{5}$ 个反应的网络的生成和动力学表征。对生成的反应网络进行符号-数值建模，用于在实验可实现的条件（如连续可变温度条件）下进行灵活、高效的动力学曲线计算，从而实现自下而上的反应网络与实验观察之间的直接联系。长时间尺度动力学曲线的高效传播是自动反应网络细化所必需的，在此通过一种新的离散动力学近似方法实现。因此，最终的 Kinetica.jl 模拟包能够实现复杂化学反应系统的自动生成、表征和长时间尺度建模。我们以在数秒时间尺度上模拟的烃类热解为例，使用代表管式流动反应器实验的瞬态温度曲线对此进行了演示。
英文摘要：
Predicting the degradation processes of molecules over long timescales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully-automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing $\sim10^{3}$ chemical species and $10^{4}$ - $10^{5}$ reactions. Symbolic-numeric modelling of the generated reaction networks is used to allow for flexible, efficient computation of kinetic profiles under experimentally-realizable conditions such as continuously-variable temperature regimes, enabling direct connection between bottom-up reaction networks and experimental observations. Highly efficient propagation of long-timescale kinetic profiles is required for automated reaction network refinement and is enabled here by a new discrete kinetic approximation. The resulting Kinetica.jl simulation package therefore enables automated generation, characterization, and long-timescale modelling of complex chemical reaction systems. We demonstrate this for hydrocarbon pyrolysis simulated over timescales of seconds, using transient temperature profiles representing those of tubular flow reactor experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
