发表机构
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种集成工作流程，结合高通量DFT、自动反应网络构建和反应-扩散动力学模拟，无需预设即可发现凝聚相辐射分解中的复杂反应机制，并成功应用于EUV光刻胶体系，揭示初始电离物种对产物分布的关键影响。
AI 中文摘要
许多重要的化学系统，从辐射驱动过程到凝聚相光化学，都涉及极其复杂的反应机制，以至于通过实验表征或基于化学直觉进行预测都颇具挑战。现有的机制发现计算方法通常仅通过热力学有利性来评估路径的重要性，这既不提供时间相关的动力学信息，也无法指导如何模拟空间不均匀性。在此，我们描述了一种集成工作流程，该流程无需预设即可发现复杂反应机制，并将分子尺度的反应性与时空可观测现象联系起来。该工作流程结合了高通量密度泛函理论（DFT）、带有化学合理性过滤的自动反应网络构建、用于识别可能发生反应的随机路径采样，以及在空间和时间上显式追踪物种的具有空间分辨率的反应-扩散动力学模拟。为了在高复杂度系统上演示该工作流程，我们将其应用于极紫外（EUV）有机聚合物薄膜光刻胶中的辐射化学，其中单个92 eV光子会在纳米尺度的辐射刺迹中引发自由基离子、碎片和低能电子的级联。从超过3300种物种和数百万个候选反应出发，该工作流程识别出最可能的反应路径，并生成在15.5纳米域内于飞秒至纳秒时间尺度上解析产物形成的时空图谱。模拟预测了实验检测到的产物，并揭示初始光电离物种的身份通过多步路径深刻影响下游产物分布，这些路径调控着脱保护与交联反应之间的平衡。该方法广泛适用于复杂的凝聚相反应系统。
英文摘要
Many important chemical systems, from radiation-driven processes to condensed-phase photochemistry, involve reaction mechanisms that are so complex it is a challenge to characterize them experimentally or predict them from chemical intuition. Existing computational approaches to mechanism discovery typically assess pathway importance through thermodynamic favorability alone, which does not provide time-dependent kinetics or inform how to model spatial inhomogeneities. Here we describe an integrated workflow that discovers complex reaction mechanisms without prescribing them and connects molecular-scale reactivity to spatiotemporal observables. The workflow combines high-throughput DFT, automated reaction network construction with chemical plausibility filtering, stochastic pathway sampling to identify reactions which are likely to occur, and spatially resolved reaction-diffusion kinetics simulations with explicit tracking of species in space and time. To demonstrate the workflow on a system of high complexity, we apply it to radiolytic chemistry in an extreme ultraviolet (EUV) organic polymer thin film photoresist, where a single 92 eV photon initiates cascades of radical ions, fragments, and low-energy electrons across a nanoscale radiolytic spur. Starting from over 3,300 species and millions of candidate reactions, the workflow identifies the most likely reaction pathways and produces spatiotemporal maps that resolve product formation on femtosecond-to-nanosecond timescales across a 15.5-nm domain. The simulations predict products detected experimentally and reveal that the identity of the initially photoionized species profoundly shapes the downstream product distribution through multi-step pathways governing the balance between deprotection and crosslinking reactions. The methodology is broadly applicable to complex condensed-phase reactive systems.
