Mechanisms investigation of coal pyrolysis will aid efficient and clean coal conversion and utilization. However, coal pyrolysis is a complex process involving myriad coupled reaction pathways such that the deeper understanding of its mechanism is still limited even with state-of-the-art experimental approaches. In this paper, ReaxFF molecular dynamics simulation was employed to perform simulation of chemical reactions in pyrolysis of a bituminous coal model with 4976 atoms to examine the nascent decomposition mechanisms and product profiles at temperatures from 1000 to 2000 K over a 250 ps simulation period. It is found that more than 900 reactions may occur at the temperature 2000 K within the simulation period with a trajectory output interval of 12.5 ps, and a detailed chemical reaction network was obtained by further analysis of the trajectory using a newly created C++ program. The product profile evolution tendency with temperature observed in the simulation agrees well with what was obtained experimentally in the literature. In addition, the sequence of gas generation was H2O, CO2, CO, C2H6, and then CH4 consistent with experimental observations. We believe that the methodology presented in this paper offers a new and promising approach to systematically build understanding of the complex chemical reactions in thermolysis of very complicated molecular systems.

Reactive force field (ReaxFF), a recent and novel bond order potential, allows for reactive molecular dynamics (ReaxFF MD) simulations for modeling larger and more complex molecular systems involving chemical reactions when compared with computation intensive quantum mechanical methods. However, ReaxFF MD can be approximately 10–50 times slower than classical MD due to its explicit modeling of bond forming and breaking, the dynamic charge equilibration at each time-step, and its one order smaller time-step than the classical MD, all of which pose significant computational challenges in simulation capability to reach spatio-temporal scales of nanometers and nanoseconds. The very recent advances of graphics processing unit (GPU) provide not only highly favorable performance for GPU enabled MD programs compared with CPU implementations but also an opportunity to manage with the computing power and memory demanding nature imposed on computer hardware by ReaxFF MD. In this paper, we present the algorithms of GMD-Reax, the first GPU enabled ReaxFF MD program with significantly improved performance surpassing CPU implementations on desktop workstations. The performance of GMD-Reax has been benchmarked on a PC equipped with a NVIDIA C2050 GPU for coal pyrolysis simulation systems with atoms ranging from 1378 to 27,283. GMD-Reax achieved speedups as high as 12 times faster than Duin et al.’s FORTRAN codes in Lammps on 8 CPU cores and 6 times faster than the Lammps’ C codes based on PuReMD in terms of the simulation time per time-step averaged over 100 steps. GMD-Reax could be used as a new and efficient computational tool for exploiting very complex molecular reactions via ReaxFF MD simulation on desktop workstations.Graphical abstractFigure optionsDownload full-size imageDownload high-quality image (150 K)Download as PowerPoint slideHighlights► ReaxFF MD allows for faster modeling of larger chemical reactive systems than QM. ► We present the novel design and algorithms of GMD-Reax. ► GMD-Reax is the first GPU enabled ReaxFF MD program implemented on a single GPU. ► Reasonable accuracy was obtained in GMD-Reax benchmarks. ► The performance of GMD-Reax is up to 12 times faster than Lammps on 8 CPU cores.