The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method.

https://biblio.ugent.be/publication/8514189/file/8514192.pdf

The ReaxFF reactive force-field: development, applications and future directions

Molecular modeling is applied to a representative array of kerogens for the purpose of obtaining quantitative predictions of thermodynamic properties from quantum mechanics and volumetric properties from molecular dynamics. The kerogen model units (175–260 carbon atoms) have been built in the MedeA environment from the sole consideration of the elemental analysis and functional group analysis documented in the work of Exxon and IFP-EN scientists [Kelemen, S. R., et al., Energy Fuels, 2007, 21 (3), pp 1548–1561]. The density results are in good agreement with the well-documented trends of kerogen density with thermal maturity and organic type. The heat capacity in the ideal gas state is predicted to increase as a function of temperature, as obtained from quantum mechanics at the semiempirical level (MOPAC-PM7). This result is in quantitative agreement with experimental heat capacity data on type I kerogen and on coal. This behavior appears clearly as a nonclassical feature, because of the quantization of e...

Molecular Modeling of the Volumetric and Thermodynamic Properties of Kerogen: Influence of Organic Type and Maturity

Pyrolysis has created many (and will open more) possibilities for high-value utilization of biomass. To obtain the optimal amount of desired pyrolysis products, especially high-quality bio-oil, a g...

A Review of Recent Advances in Biomass Pyrolysis

Chemical understanding is driven by the experimental discovery of new compounds and reactivity, and is supported by theory and computation that provides detailed physical insight. While theoretical and computational studies have generally focused on specific processes or mechanistic hypotheses, recent methodological and computational advances harken the advent of their principal role in discovery. Here we report the development and application of the ab initio nanoreactor – a highly accelerated, first-principles molecular dynamics simulation of chemical reactions that discovers new molecules and mechanisms without preordained reaction coordinates or elementary steps. Using the nanoreactor we show new pathways for glycine synthesis from primitive compounds proposed to exist on the early Earth, providing new insight into the classic Urey-Miller experiment. These results highlight the emergence of theoretical and computational chemistry as a tool for discovery in addition to its traditional role of interpreting experimental findings.

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Discovering chemistry with an ab initio nanoreactor

A detailed insight of key reactive events related to oxidation and pyrolysis of hydrocarbon fuels further enhances our understanding of combustion chemistry. Though comprehensive kinetic models are available for smaller hydrocarbons (typically C3 or lower), developing and validating reaction mechanisms for larger hydrocarbons is a daunting task, due to the complexity of their reaction networks. The ReaxFF method provides an attractive computational method to obtain reaction kinetics for complex fuel and fuel mixtures, providing an accuracy approaching ab-initio-based methods but with a significantly lower computational expense. The development of the first ReaxFF combustion force field by Chenoweth et al. (CHO-2008 parameter set) in 2008 has opened new avenues for researchers to investigate combustion chemistry from the atomistic level. In this article, we seek to address two issues with the CHO-2008 ReaxFF description. While the CHO-2008 description has achieved significant popularity for studying large ...

Extension of the ReaxFF Combustion Force Field toward Syngas Combustion and Initial Oxidation Kinetics

In this study, the first GPU-enabled ReaxFF MD program with significantly improved performance, surpassing CPU implementations, was employed to explore the initial chemical mechanisms and product distributions in pyrolysis of Liulin coal, a bituminous coal from Shanxi, PRC. The largest coal model ever used in simulation via ReaxFF MD, the Liulin coal molecular model consisting of 28 351 atoms was constructed based on a combination of experiments and classical coal models. The ReaxFF MD simulations at temperatures of 1000-2600 K were performed for 250 Ps to investigate the temperature effects on the product profile and the initial chemical reactions of the Liulin coal model pyrolysis. The generation rates of C-14-C-40 compounds and gas tend to equilibrate within 150-250 ps, indicating that the simulation should allow most of the thermal decomposition reactions complete and the simulated product profiles are reasonable for understanding the chemical reactions of the Liulin coal pyrolysis. The product (gas, tar, and char) evolution tendencies with time and temperature observed in the simulations are fairly in agreement with the experimental tendency reported in the literature. In particular, the evolution trends of three representative products (naphthalene, methyl-naphthalene and dimethyl-naphthalene) with temperature are very consistent with Py-GC/MS experiments. The detailed chemical reactions of the pyrolysis simulation have been generated using VARMD (Visualization and Analysis of Reactive Molecular Dynamics), which was newly created to examine the complexity of the chemical reaction network in ReaxFF MD simulation. The generation and consumption of HO center dot and H3C. radicals with time and temperature are reasonable and consistent both with the evolution of H2O and CH4, and with the detailed chemical reactions obtained as well. The amount of six-membered ring structures was observed to decrease with time and temperature, because of their conversion into 5-membered rings or 7-9-membered rings or even-larger-membered ring structures that will further open and decompose into small fragments. This work demonstrates a new methodology for investigating coal pyrolysis mechanism by combining GPU-enabled high-performance computing with cheminformatics analysis in ReaxFF MD.

Pyrolysis of Liulin Coal Simulated by GPU-Based ReaxFF MD with Cheminformatics Analysis

Initial reaction mechanisms of cellulose pyrolysis revealed by ReaxFF molecular dynamics

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 experi...

Initial Chemical Reaction Simulation of Coal Pyrolysis via ReaxFF Molecular Dynamics

Initial reaction mechanisms of lignin pyrolysis were studied by large-scale ReaxFF molecular dynamics simulations (ReaxFF MD) facilitated by the first GPU-enabled code (GMD-Reax) and the unique reaction analysis tool (VARxMD). Simulations were performed over wide temperature ranges both for heat up at 300–2100 K and for NVT at 500–2100 K with a large lignin model, which contained 15920 atoms and was constructed based on Adler’s softwood lignin model. By utilizing the relatively continuous observation for pyrolysate evolution in slow heat up simulations, three stages for lignin pyrolysis are proposed by pyrolysate fractions. The underlying mechanisms for the three stages are revealed by analyzing the species structure evolution and the reactions of linkages, aryl units, propyl chains, and methoxy substituents. Stage I is characterized with the complete decomposition of source lignin molecules at low temperatures dominated by breaking of α-O-4 and β-O-4 linkages. The temperature in stage II is relatively hi...

Initial Mechanisms for an Overall Behavior of Lignin Pyrolysis through Large-Scale ReaxFF Molecular Dynamics Simulations

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. (C) 2013 Elsevier Inc. All rights reserved.

Mo Zheng

Papers

Pyrolysis of Liulin Coal Simulated by GPU-Based ReaxFF MD with Cheminformatics Analysis

Initial reaction mechanisms of cellulose pyrolysis revealed by ReaxFF molecular dynamics

Initial Chemical Reaction Simulation of Coal Pyrolysis via ReaxFF Molecular Dynamics

Initial Mechanisms for an Overall Behavior of Lignin Pyrolysis through Large-Scale ReaxFF Molecular Dynamics Simulations

Algorithms of GPU-enabled reactive force field (ReaxFF) molecular dynamics.