Understanding cellulose pyrolysis via ab initio deep learning potential field

Yuqin Xiao, Yuxin Yan, Hainam Do, Richard Rankin, Haitao Zhao, Ping Qian, Keke Song, Tao Wu, Cheng Heng Pang

Research output: Journal PublicationArticlepeer-review


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.

Original languageEnglish
Article number130590
JournalBioresource Technology
Publication statusPublished - May 2024


  • Biomass degradation
  • Chemical calculations
  • Dynamics simulation
  • Mechanism study
  • Reaction path

ASJC Scopus subject areas

  • Bioengineering
  • Environmental Engineering
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal


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