Comprehensive and dynamic studies of cellulose pyrolysis reaction mechanisms are crucial in designing experiments and processes with enhanced safety, efficiency, and sustainability. The details of the pyrolysis mechanism are not readily available from experiments but can be better described via molecular dynamics (MD) simulations. However, the large size of cellulose molecules challenges accurate ab initio MD simulations, while existing reactive force field parameters lack precision. In this work, precise ab initio deep learning potentials field (DPLF) are developed and applied in MD simulations to facilitate the study of cellulose pyrolysis mechanisms. The formation mechanism and production rate of both valuable and greenhouse products from cellulose at temperatures larger than 1073 K are comprehensively described. This study underscores the critical role of advanced simulation techniques, particularly DLPF, in achieving efficient and accurate understanding of cellulose pyrolysis mechanisms, thus promoting wider industrial applications.

中文翻译：

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通过从头算深度学习势场了解纤维素热解

纤维素热解反应机制的全面和动态研究对于设计提高安全性、效率和可持续性的实验和工艺至关重要。热解机制的细节不易从实验中获得，但可以通过分子动力学（MD）模拟更好地描述。然而，大尺寸的纤维素分子对精确的从头开始MD模拟提出了挑战，而现有的反作用力场参数缺乏精度。在这项工作中，开发了精确的从头算深度学习势场（DPLF）并将其应用于MD模拟，以促进纤维素热解机制的研究。全面描述了在高于 1073 K 的温度下纤维素有价值的温室产品的形成机制和生产率。这项研究强调了先进模拟技术（尤其是 DLPF）在高效、准确地理解纤维素热解机制方面的关键作用，从而促进更广泛的工业应用。