Xiaoxia Li. Group of HPC & Cheminformatics Institute of Process Engineering Chinese Academy of Sciences, Beijing

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1 GTC 2016 San Jose, Californiae, 7 April, 2016 Xiaoxia Li Group of HPC & Cheminformatics Institute of Process Engineering Chinese Academy of Sciences, Beijing

2 Outline Reaction mechanisms of coal pyrolysis? GPU-enabled ReaxFF MD (GMD-Reax) Pyrolysis of coal, biomass, polymer 4 Concluding remarks and perspective 2

3 Background China is the largest producer & consumer of coal China has much more coal, less oil Reaction mechanism? 3 Mechanism still hardly accessible Experimentally, hard to detect and replicate the free radical initiation at high temperature in lab Computationally with QM, extremely high computing cost, limited model scale: ~100 atoms ReaxFF MD (Reactive molecular dynamics)

Overview of ReaxFF ReaxFF MD: reactive force field + molecular dynamics Publications on ReaxFF MD Subject searching hits from Web of Science by van Duin (Penn state), Goddard (Caltech) et al.

4 Overview of ReaxFF ReaxFF MD: reactive force field + molecular dynamics Publications on ReaxFF MD Subject searching hits from Web of Science by van Duin (Penn state), Goddard (Caltech) et al. for bond breaking and forming with parameters based on experiments and QM (quantum mechanics approach) Faster than DFT (widely used QM) for models > 1000 atoms No priori knowledge of reaction pathways required A comprehensive knowledge on multiple reaction pathways of coal pyrolysis is not available! ReaxFF MD is promising for coal pyrolysis simulation 4

Can large coal model simulated efficiently with ReaxFF? HPC Programs of ReaxFF - supercomputer/cluster F-ReaxFF, Univ. South. California, 2007 (parallel ) PuReMD, Purdue Univ.

5 Can large coal model simulated efficiently with ReaxFF? HPC Programs of ReaxFF - supercomputer/cluster F-ReaxFF, Univ. South. California, 2007 (parallel ) PuReMD, Purdue Univ., 2011 (single node performance ) In LAMMPS, Sandia National Lab. (open source) FORTRAN code (precise, based on van Duin s original code) C code (2011, faster, based on PuReMD) In commercial software ADF (to enhance visualization, ~2011) GULP, Materials Studio 6.0 (2012) Desktop workstation is more preferable Is it practical to simulate large coal model (~10,000 atoms) on desktop workstation? 5

~10,000 atoms, state-of-the-art coal model scale ~1,000 atoms, practical

QM software) on single computational node ReaxFF vs LJ potential LAMMPS

6 ReaxFF MD on Desktop workstation? Computational challenges complexity of coal structure and pyrolysis ~10,000 atoms, state-of-the-art coal model scale ~1,000 atoms, practical scale for LAMMPS (Sandia National Lab) and ADF (Europe, a major player of QM software) on single computational node ReaxFF vs LJ potential LAMMPS Benchmarks 2012: bench.html#potentials) folds slower than classical MD C code FORTRAN code 6

7 MD Overview of ReaxFF MD ReaxFF MD Dynamic atom charge equilibration Fixed atom charge Time-step 1 fs Bond order dependency Time-step 0.1 fs 7

8 Computational cost of ReaxFF MD vs MD ReaxFF MD vs MD Similar computing loops, but Time-step: 0.1 fs (ReaxFF MD) vs 1 fs (MD) Atom charge: optimizing at each time-step (ReaxFF MD) vs fixed (MD) Additional computing introduced in potential & its corrections Taper + Morse for van der Waals in ReaxFF 8

2007 MD codes (major players and novel codes such as HOOMD) Stone, J.E.

Journal of Molecular Graphics and Modelling, 2010. 29(2): p. 116-125.

5 (GPU enabled) 1 Peta, Double Top 500 Supercomputer 19 th, 2010 33

9 ReaxFF MD on Desktop workstation? GPU Thanks for the GPU & CUDA Rapid development GPU computing since 2007 MD codes (major players and novel codes such as HOOMD) Stone, J.E., et al., GPU-accelerated molecular modeling coming of age. Journal of Molecular Graphics and Modelling, (2): p GPU infrastructure in IPE (in my office building) Mole-8.5 (GPU enabled) 1 Peta, Double Top 500 Supercomputer 19 th, th, th, th, 2013 Potential seen from GMD we created in (a GPU enabled code for MD) Polyethylene crystalization 9

models: 360,000 united atoms & 400,000 united atoms Simiao Wang, et al.

10 GMD and its applications in polymer crystallization study GMD: a GPU enabled code for classical MD Our first attempt using GPU Performance is comparable with early version of GROMACS GPU Application in polymer chain crystallization (Polyethylene as model) PE models: 360,000 united atoms & 400,000 united atoms Simiao Wang, et al. Two mechanisms of polymer chain crystallization within nanoglobule. Polymer. 2013;54(15): folds larger model scale than that simulated in CPU cluster Students in GPU HPC companies (NVIDIA, Sugon) and more

11 GMD-Reax: ReaxFF MD on GPU GPU works for MD the first GPU code for ReaxFF MD (C2050) Its implementation tough job Constrained coding closely linked with GPU hardware faster memory limited, global memory access latency, and more 11

12 GMD-Reax: ReaxFF MD on GPU Our approach Most of computations on GPU Faster SFU for some bond order based corrections (early version) T thread for charge evaluation/time-step bottle neck Finely tuned data access for computation, and more 12

13 GMD-Reax: performances GMD-Reax on one C2050 achieved up to 16 times speedup against the LAMMPS codes on 8 CPUs (~fastest on CPU, Sandia National Lab & Purdue Univ) Single precision 13 Zheng, M.; Li, X.; Guo, L., Algorithms of GPU-enabled reactive force field (ReaxFF) molecular dynamics. Journal of Molecular Graphics and Modelling 2013, 41, (April), 1-11

14 GMD-Reax: performances GMD-Reax on one C2050 achieved up to 8 times speedup against the LAMMPS codes on 8 CPUs (~fastest on CPU, Sandia National Lab & Purdue Univ) Double precision 14

15 GMD-Reax: performance & impact GMD-Reax (Ours, DP) PuReMD-GPUs (Purdue Univ.) Systems Benchmarked Bulk water systems Amorphous coal pyrolysis ( atoms) systems Amorphous silica ( atoms) ( atoms) Hardware of GPU Tesla C2050 Tesla C2075 Speedups against (water) PuReMD in LAMMPS (complex coal models) (silica) (1 CPU core) Speedups against PuReMD in LAMMPS (8 CPU cores) (complex coal models) (water) (silica) Notes Coal models are more complex than bulk water or silica systems, of which all energy terms must be computed in potential evaluation of ReaxFF MD Tesla C2075 has more global memory than Tesla C2050 Ours : Journal of Molecular Graphics and Modelling 2013, 41, (April), 1-11 Top 5, NVIDIA GPU Award, 248 th ACS meeting, 2014 PuReMD-GPUs: Journal of Computational Physics 2014, 272(Sept), The only two GPU codes available have comparable performance, ours even better Ours published ~ 1.5 year earlier

ReaxFF MD of coal pyrolysis Challenges complexity of coal structure and

Lack of reaction analysis ability for revealing mechanism LAMMPS, ADF

) number of molecules (formula based) ~ time Manual analysis is a must?

16 ReaxFF MD of coal pyrolysis Challenges complexity of coal structure and pyrolysis Coal model construction? Computing scale discrepancy? Lack of reaction analysis ability for revealing mechanism LAMMPS, ADF analysis tool (?) number of molecules (formula based) ~ time Manual analysis is a must? 16 Manual analysis is not practical for revealing the complex reaction mechanism of coal pyrolysis n-dodecane (C 6 H 14 ) pyrolysis: 1279 species, 5056 reactions

17 VARxMD: the first reaction analysis tool for ReaxFF MD What we need to do? Reaction analysis - discovering the bonding and species changes 3D chemical structure processing Automatic perception of atomic connectivity, bonding type, species, reaction 17 Jian Liu, Xiaoxia Li et al., Journal of Molecular Graphics and Modelling 2014, 53(9):13-22

18 VARxMD: the first reaction analysis tool for ReaxFF MD What we have detailed reaction list All reactions Product evolution & underlying reactions 2D & 3D Reaction details 18 Allowing for direct observation of chemistry events computationally

19 VARxMD: the first reaction analysis tool for ReaxFF MD What we have a view of all reaction sites 19 Allowing for direct observation of chemistry events computationally

20 VARxMD: the first reaction analysis tool for ReaxFF MD What we have a 3D view of a reaction with reaction sites highlighted 20 Reaction site bond breaking or forming highlighted

21 New methodology for large scale ReaxFF MD GPU high performance computing We created the first GPU-enabled codes 21 Cheminformatics approach We created the first reaction analysis tool Xiaoxia Li et al., Molecular Simulation, 2015, 41(1-3), 13-27

22 New methodology applications Large scale ReaxFF MD simulations Coal pyrolysis (~10,000 atoms) Liulin coal model: C14782H12702N140O690S37, 28,351 atoms, second largest ever simulated Pyrolysis of polymer (HDPE) (150x8, 7216 atoms) Pyrolysis of biomass 15,920 atoms for lignin 7572 atoms (C2160H3612O1800) Pyrolysis and oxidation hydrocarbon fuel 10,828 atoms for bio-oil Typical time for one condition is one week (GMD-Reax) 22 Tingting Zhang, Xiaoxia Li, et al. Energy and Fuels 2016, just accepted Mo Zheng, Ze Wang, Xiaoxia Li, et al. Fuel, : p Xiaolong Liu, Xiaoxia Li, et al. Polymer Degradation and Stability 2014, 104(June), Mo Zheng, Xiaoxia Li, et al. Energy and Fuels 2014, 28(1),

New methodology applications Coal pyrolysis simulations - large scale coal models Models Model scale (atoms) Chemical formula Simulation time ~

N 8 O 1137 S 16 (<2 days) ~ 6000 min Hailaer brown coal model 27,809 C 12996 H 12228 N 18 O 2561 S 36 (4 days) ~ 2800 min Liulin bituminous coal

5 days) ~ 6000 min Fugu subbituminous coal model 23,898 C 11995 H 10362 N 159 O 1366 S 15 (4.

23 New methodology applications Coal pyrolysis simulations - large scale coal models Models Model scale (atoms) Chemical formula Simulation time ~ 7000 min Bituminous model (proof-of- concept) 4976 C 2417 H 2235 N 41 O 240 S 43 (5 days) ~ 2400 min Hailaer brown coal model 12,335 C 5752 H 5422 N 8 O 1137 S 16 (<2 days) ~ 6000 min Hailaer brown coal model 27,809 C H N 18 O 2561 S 36 (4 days) ~ 2800 min Liulin bituminous coal model 13,498 C 7068 H 5968 N 78 O 351 S 33 (2 days) ~ 6300 min Liulin bituminous coal model 28,351 C H N 140 O 690 S 37 (4.5 days) ~ 6000 min Fugu subbituminous coal model 23,898 C H N 159 O 1366 S 15 (4.0 days) Proximate and Ultimate Analysis of Liulin Coal 23 Ultimate Analysis (wt % daf) C 88.4 H 4.8 O 5.2 N 0.94 S 0.46 Proximate analysis ( wt% ) Moisture 0.66 Ash Volatile C-NMR spectra of Liulin coal

Liulin bituminus coal High temperature and short

24 New methodology applications Coal pyrolysis simulations evolution of overall spectrum products Liulin bituminus coal High temperature and short time pyrolysis favor the maximum amount of tar generation 24

Representative products/precursors in coal tar Liulin bituminous coal pyrolysis Py-GC/MS, up to 20,000 K/s heating rate Advantage of our VARxMD & large scale models (~30,000 atoms) 25

25 Representative products/precursors in coal tar Liulin bituminous coal pyrolysis Py-GC/MS, up to 20,000 K/s heating rate Advantage of our VARxMD & large scale models (~30,000 atoms) 25 Naphthalene, methyl-naphthalene and dimethyl-naphthalene are representative products in Liulin coal pyrolysis observed by Py-GC/MS Simulated observation within 87.5 ps agree with Py-GC/MS

New methodology applications Coal pyrolysis reaction mechanisms - by the unique VARxMD Complex radical reactions newly revealed No Reactions involving H 3 C Reactions involving HO H 3 C and HO

+ C 312H 259O 16N 3 S C 281H 229O 14N 2 S+ CHO + C 30H 28N + C 30 H 28 N+C 5 H 5 +CH 3 HO 3 C 312 H 259 O 16 N 3 S C 291 H 236 O 12 N 3 S +CHO+HO + CHO 2 + C 275H 219O 14N 2 +HS C 275H 215O 11N 2

26 New methodology applications Coal pyrolysis reaction mechanisms - by the unique VARxMD Complex radical reactions newly revealed No Reactions involving H 3 C Reactions involving HO H 3 C and HO generation 1 C 312 H 259 O 16 N 3 S C 281 H 228 O 16 N 2 S +C 30 H 28 N + CH 3 C 312 H 259 O 16 N 3 S C 307 H 253 O 15 N 3 S + HO + C 5 H 5 2 C 312 H 259 O 16 N 3 S C 275 H 220 O 12 N 2 S +2HO+CHO 2 + C 312H 259O 16N 3 S C 281H 229O 14N 2 S+ CHO + C 30H 28N + C 30 H 28 N+C 5 H 5 +CH 3 HO 3 C 312 H 259 O 16 N 3 S C 291 H 236 O 12 N 3 S +CHO+HO + CHO 2 + C 275H 219O 14N 2 +HS C 275H 215O 11N 2 S+2H 2 O+ HO C 18 H 17 + CH 3 4 C 312 H 259 O 16 N 3 S C 310 H 254 O 13 N 3 S +HO + CHO 2 + CH 3 C 65 H 51 O 3 N C 65 H 50 O 2 N + HO 5 CH 3 + C 22 H 22 O CH 4 + C 22 H 21 O C 28 H 28 O+ HO C 28 H 27 O+H 2 O 6 C 281 H 226 O 14 N 2 S+2HO+CH 3 C 282 H 230 O 16 N 2 + HS C 272 H 215 O 11 N 2 S+HO+C 24 H 22 ON+ C 13 H 8 C 13 H 7 + C 47 H 37 O 2 N+C 225 H 179 O 10 NS+ H 2 + C 24 H 21 ON H 3 C and HO consumption 26 7 CH 3 + C 24 H 23 ON CH 4 + C 24 H 21 N+ HO HO+ C 16 H 16 H 2 O+C 16 H 15 8 CH 3 + C 276 H 225 O 14 N 2 S+C 30 H 28 N CH 4 + C 306 H 251 O 13 N 3 S+ HO HO+ C 193 H 154 O 12 NS+CH 3 H 2 O+ C 35 H 30 O 2 N + C 159 H 125 O 10 +HS Coal pyrolysis is initialized by thermal decomposition at bridged bonds of coal structure to produce unstable radicals such as HO and H 3 C

27 New methodology applications Coal pyrolysis simulations correlation of radicals and products 27 at low T, H 3 C fluctuating few CH 4 generated at high T, H 3 C decreasing - increased production of CH 4 Earlier maximum and then decreasing of HO increasing of H 2 O with T

28 New methodology applications HDPE pyrolysis simulations Reproduce comprehensive reaction mechanism & weight loss time prediction 150x8, 7216 atoms ReaxFF MD simulation Py-GC/MS experiment 28

29 New methodology applications Cellulose pyrolysis simulations Product evolutions & major reaction pathways 6*60 1,4-β-D-glucopyranoses 7572/17664 atoms 29

30 New methodology applications Lignin pyrolysis simulations Three pyrolysis stages & reaction mechanism 40xC 160 H 180 O atoms 30

31 Summary & perspective New methodology for ReaxFF MD: GPU computing + cheminformatics GMD-Reax first GPU code of ReaxFF MD, much faster VARxMD a novel tool, unravel of complex detailed reactions Large scale pyrolysis simulation of polymer, biomass & coal reaction mechanisms revealed hardly accessible experimentally or by QM, or by small scale simulations Methodology application perspective GMD-Reax can be used in other ReaxFF MD applications for combustion, catalysis etc. VARxMD can be applied too Approaching to more real process of pyrolysis and combustion Working with models of 100,000 atoms on one single workstation with GPUs 31

32 Acknowledgment Grants of NSFC ( , ), MPCS-2012-A-05 Hard work of Xiaolong Liu Dr. Xianjie Qiao Tingting Zhang Chunxing Reng Dr. Mo Zheng Junyi Han Jian Liu Mingjie Gao Xiaomin Gong Song Han Xiaofang Tao Zimin Wang Dr. Ze Wang Prof Li Guo Prof Wenli Song Prof. Fengguang Nie Dr. Zhaojie Xia Wucheng Tang

Perm State University Research-Education Center Parallel and Distributed Computing

Perm State University Research-Education Center Parallel and Distributed Computing A 25-minute Talk (S4493) at the GPU Technology Conference (GTC) 2014 MARCH 24-27, 2014 SAN JOSE, CA GPU-accelerated modeling