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Discovering chemistry with an ab initio nanoreactor

Nature Chemistry volume 6, pages 1044–1048 (2014)Cite this article

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A Corrigendum to this article was published on 17 December 2014

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Abstract

Chemical understanding is driven by the experimental discovery of new compounds and reactivity, and is supported by theory and computation that provide detailed physical insight. Although 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, which provide 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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Figure 1: Timeline of a nanoreactor simulation trajectory (see Supplementary Video 1 for a movie clip).
Figure 2: Pyramid representation of reaction network focused on a product molecule of interest (3D view in Supplementary Video 2).
Figure 3: Sequence of elementary reaction steps derived from the nanoreactor simulation.

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  • 20 November 2014

    In the version of this Article originally published, the list of author affiliations was incomplete, and should have appeared as shown below. The list has been corrected in the online versions of the Article. Lee-Ping Wang1,2, Alexey Titov1,2✝, Robert McGibbon2, Fang Liu1,2, Vijay S. Pande2 and Todd J. Martínez1,2,3* 1The PULSE Institute, Stanford University, Stanford, California 94305, USA. 2Department of Chemistry, Stanford University, Stanford, California 94305, USA. 3SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA. ✝Present address: Advanced Micro Devices, Sunnyvale, California 94088, USA. *e-mail: toddjmartinez@gmail.com

References

  1. Ufimtsev, I. S. & Martinez, T. J. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J. Chem. Theory Comput. 5, 2619–2628 (2009).

    Article  CAS  Google Scholar 

  2. Ufimtsev, I. S., Luehr, N. & Martinez, T. J. Charge transfer and polarization in solvated proteins from ab initio molecular dynamics. J. Phys. Chem. Lett. 2, 1789–1793 (2011).

    Article  CAS  Google Scholar 

  3. Luehr, N., Ufimtsev, I. S. & Martinez, T. J. Dynamic precision for electron repulsion integral evaluation on graphical processing units (GPUs). J. Chem. Theory Comput. 7, 949–954 (2011).

    Article  CAS  Google Scholar 

  4. Kulik, H. J., Luehr, N., Ufimtsev, I. S. & Martinez, T. J. Ab initio quantum chemistry for protein structures. J. Phys. Chem. B 116, 12501–12509 (2012).

    Article  CAS  Google Scholar 

  5. Yin, Y. et al. Formation of hollow nanocrystals through the nanoscale Kirkendall effect. Science 304, 711–714 (2004).

    Article  CAS  Google Scholar 

  6. Ensing, B., De Vivo, M., Liu, Z. W., Moore, P. & Klein, M. L. Metadynamics as a tool for exploring free energy landscapes of chemical reactions. Acc. Chem. Res. 39, 73–81 (2006).

    Article  CAS  Google Scholar 

  7. Pietrucci, F. & Andreoni, W. Graph theory meets ab initio molecular dynamics: atomic structures and transformations at the nanoscale. Phys. Rev. Lett. 107, 085504 (2011).

    Article  Google Scholar 

  8. Iannuzzi, M., Laio, A. & Parrinello, M. Efficient exploration of reactive potential energy surfaces using Car–Parrinello molecular dynamics. Phys. Rev. Lett. 90, 238302 (2003).

    Article  Google Scholar 

  9. Zimmerman, P. M. Automated discovery of chemically reasonable elementary reaction steps. J. Comput. Chem. 34, 1385–1392 (2013).

    Article  CAS  Google Scholar 

  10. Rappoport, D., Galvin, C. J., Zubarev, D. Y. & Aspuru-Guzik, A. Complex chemical reaction networks from heuristics-aided quantum chemistry. J. Chem. Theory Comput. 10, 897–907 (2014).

    Article  CAS  Google Scholar 

  11. Virshup, A. M., Contreras-García, J., Wipf, P., Yang, W. & Beratan, D. N. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J. Am. Chem. Soc. 135, 7296–7303 (2013).

    Article  CAS  Google Scholar 

  12. Maeda, S. & Morokuma, K. Toward predicting full catalytic cycle using automatic reaction path search method: a case study on HCo(CO)3-catalyzed hydroformylation. J. Chem. Theory Comput. 8, 380–385 (2012).

    Article  CAS  Google Scholar 

  13. Wales, D. J., Miller, M. A. & Walsh, T. R. Archetypal energy landscapes. Nature 394, 758–760 (1998).

    Article  CAS  Google Scholar 

  14. Goldman, N., Reed, E. J., Fried, L. E., Kuo, I. F. W. & Maiti, A. Synthesis of glycine-containing complexes in impacts of comets on early Earth. Nature Chem. 2, 949–954 (2010).

    Article  CAS  Google Scholar 

  15. Goldman, N. et al. Ab initio simulation of the equation of state and kinetics of shocked water. J. Chem. Phys. 130, 124517 (2009).

    Article  Google Scholar 

  16. Bernasconi, M., Chiarotti, G. L., Focher, P., Parrinello, M. & Tosatti, E. Solid-state polymerization of acetylene under pressure: ab initio simulation. Phys. Rev. Lett. 78, 2008–2011 (1997).

    Article  CAS  Google Scholar 

  17. Feller, D. & Peterson, K. A. An examination of intrinsic errors in electronic structure methods using the Environmental Molecular Sciences Laboratory computational results database and the Gaussian-2 set. J. Chem. Phys. 108, 154–176 (1998).

    Article  CAS  Google Scholar 

  18. Sousa, S. F., Fernandes, P. A. & Ramos, M. J. General performance of density functionals. J. Phys. Chem. A 111, 10439–10452 (2007).

    Article  CAS  Google Scholar 

  19. Harding, M. E. et al. High-accuracy extrapolated ab initio thermochemistry. III. Additional improvements and overview. J. Chem. Phys. 128, 114111 (2008).

    Article  Google Scholar 

  20. Miller, S. L. & Urey, H. C. Organic compound synthesis on the primitive Earth. Science 130, 245–251 (1959).

    Article  CAS  Google Scholar 

  21. Trout, C. C. & Badding, J. V. Solid state polymerization of acetylene at high pressure and low temperature. J. Phys. Chem. A 104, 8142–8145 (2000).

    Article  CAS  Google Scholar 

  22. Sakashita, M., Yamawaki H. & Aoki, K. FT-IR study of the solid state polymerization of acetylene under pressure. J. Phys. Chem. 100, 9943–9947 (1996).

    Article  CAS  Google Scholar 

  23. Virshup, A. M. et al. Photodynamics in complex environments: ab initio multiple spawning quantum mechanical molecular mechanical dynamics. J. Phys. Chem. B 113, 3280–3291 (2009).

    Article  CAS  Google Scholar 

  24. Danger, G., Plasson, R. & Pascal R. Pathways for the formation and evolution of peptides in prebiotic environments. Chem. Soc. Rev. 41, 5416–5429 (2012).

    Article  CAS  Google Scholar 

  25. Menten, K. M. & Wyrowski, F. in Interstellar Molecules: Their Laboratory and Interstellar Habitat (eds Yamada, K. M. T. & Winnewisser, G.) 27–42 (Springer Tracts in Modern Physics 241, Springer, 2011).

    Book  Google Scholar 

  26. Szori, M. et al. Chemical evolution of biomolecule building blocks. Can thermodynamics explain the accumulation of glycine in the prebiotic ocean? Phys. Chem. Chem. Phys. 13, 7449–7458 (2011).

    Article  CAS  Google Scholar 

  27. Wahner, A., Mentel, T. F. & Sohn, M. Gas-phase reaction of N2O5 with water vapor: importance of heterogeneous hydrolysis of N2O5 and surface desorption of HNO3 in a large Teflon chamber. Geophys. Res. Lett. 25, 2169–2172 (1998).

    Article  CAS  Google Scholar 

  28. Kasting, J. F. Earth's early atmosphere. Science 1993, 259, 920–926.

    Article  CAS  Google Scholar 

  29. Cleaves, H. J., Chalmers, J. H., Lazcano, A., Miller, S. L. & Bada J. L. A reassessment of prebiotic organic synthesis in neutral planetary atmospheres. Origins Life Evol. Biosph. 38, 105–115 (2008).

    Article  CAS  Google Scholar 

  30. Isborn, C. M., Luehr, N., Ufimtsev, I. S. & Martinez, T. J. Excited-state electronic structure with configuration interaction singles and Tamm–Dancoff time-dependent density functional theory on graphical processing units. J. Chem. Theory Comput. 7, 1814–1823 (2011).

    Article  CAS  Google Scholar 

  31. Titov, A. V., Ufimtsev, I. S., Luehr, N. & Martinez, T. J. Generating efficient quantum chemistry codes for novel architectures. J. Chem. Theory Comput. 9, 213–221 (2013).

    Article  CAS  Google Scholar 

  32. Ufimtsev, I. S. & Martinez, T. J. Graphical processing units for quantum chemistry. Comput. Sci. Eng. 10, 26–34 (2008).

    Article  CAS  Google Scholar 

  33. Ufimtsev, I. S. & Martinez, T. J. Quantum chemistry on graphical processing units. 1. Strategies for two-electron integral evaluation. J. Chem. Theory Comput. 4, 222–231 (2008).

    Article  CAS  Google Scholar 

  34. Ufimtsev, I. S. & Martinez, T. J. Quantum chemistry on graphical processing units. 2. Direct self-consistent-field implementation. J. Chem. Theory Comput. 5, 1004–1015 (2009).

    Article  CAS  Google Scholar 

  35. Saunders, V. R. & Hillier, I. H. Level-shifting method for converging closed-shell Hartree–Fock wavefunctions. Int. J. Quantum Chem. 7, 699–705 (1973).

    Article  Google Scholar 

  36. Hu, X. & Yang, W. Accelerating self-consistent field convergence with the augmented Roothaan–Hall energy function. J. Chem. Phys. 132, 054109 (2010).

    Article  Google Scholar 

  37. Pulay, P. Convergence acceleration of iterative sequences—the case of SCF iteration. Chem. Phys. Lett. 73, 393–398 (1980).

    Article  CAS  Google Scholar 

  38. Hagberg, A. A., Schult, D. A. & Swart, P. J. in Proceedings of the 7th Python in Science Conference (eds Varoquaux, G., Vaught. T & Millman, J.) 11–15 (SciPy, 2008).

    Google Scholar 

  39. Pedregosa F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  40. Becke, A. D. Density-functional thermochemistry. 3. The role of exact exchange. J. Chem. Phys. 98, 5648–5652 (1993).

    Article  CAS  Google Scholar 

  41. Guner, V. et al. A standard set of pericyclic reactions of hydrocarbons for the benchmarking of computational methods: the performance of ab initio, density functional, CASSCF, CASPT2, and CBS-QB3 methods for the prediction of activation barriers, reaction energetics, and transition state geometries. J. Phys. Chem. A 107, 11445–11459 (2003).

    Article  CAS  Google Scholar 

  42. Swart, M., Sola, M. & Bickelhaupt, F. M. Energy landscapes of nucleophilic substitution reactions: a comparison of density functional theory and coupled cluster methods. J. Comput. Chem. 28, 1551–1560 (2007).

    Article  CAS  Google Scholar 

  43. Van Voorhis, T. and Head-Gordon, M. Benchmark variational coupled cluster doubles results. J. Chem. Phys. 113, 8873–8879 (2000).

    Article  CAS  Google Scholar 

  44. Zhang, J. and Valeev, E. F. Prediction of reaction barriers and thermochemical properties with explicitly correlated coupled-cluster methods: a basis set assessment. J. Chem. Theory Comput. 8, 3175–3186 (2012).

    Article  CAS  Google Scholar 

  45. Peters, B., Heyden, A., Bell, A. T. & Chakraborty, A. A growing string method for determining transition states: comparison to the nudged elastic band and string methods. J. Chem. Phys. 120, 7877–7886 (2004).

    Article  CAS  Google Scholar 

  46. Shao, Y. et al. Advances in methods and algorithms in a modern quantum chemistry program package. Phys. Chem. Chem. Phys. 8, 3172–3191 (2006).

    Article  CAS  Google Scholar 

  47. Bui, P., Rajan, D., Abdul-Wahid, B., Izaguirre, J. & Thain, D. Work Queue + Python: a framework for scalable scientific ensemble applications. Workshop on Python for High Performance and Scientific Computing (PyHPC, 2011).

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Acknowledgements

This work was supported by the National Science Foundation (OCI-1047577), the National Institutes of Health (U54 GM072970) and the Department of Defense through a National Security Science and Engineering Faculty Fellowship from the Office of the Assistant Secretary of Defense for Research and Engineering. This work included calculations performed on the Blue Waters supercomputer at the National Centre for Supercomputing Applications and funded by the National Science Foundation's Office of Cyber Infrastructure. Further computational support was provided by the AMOS program within the Chemical Sciences, Geosciences and Biosciences Division of the Office of Basic Energy Sciences, Office of Science, Department of Energy. We are grateful to E. G. Hohenstein, N. Luehr, S. D. Fried, S. Izmailov, Y. Zhao and C-Y. Wang for helpful suggestions.

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Author notes

  1. Alexey Titov

    Present address: Present address: Advanced Micro Devices, Sunnyvale, California 94088, USA,

Authors and Affiliations

  1. The PULSE Institute, Stanford University, Stanford, 94305, California, USA

    Lee-Ping Wang, Alexey Titov, Fang Liu & Todd J. Martínez

  2. Department of Chemistry, Stanford University, Stanford, 94305, California, USA

    Lee-Ping Wang, Alexey Titov, Robert McGibbon, Fang Liu, Vijay S. Pande & Todd J. Martínez

  3. SLAC National Accelerator Laboratory, Menlo Park, 94025, California, USA

    Todd J. Martínez

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Contributions

L-P.W., A.T., F.L. and T.J.M. designed the nanoreactor simulation studies. L-P.W., R.M., V.S.P. and T.J.M. designed the energy refinement and network analysis. L.P.W. carried out the simulations and analysis. L-P.W., V.S.P. and T.J.M. co-wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Todd J. Martínez.

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The authors declare no competing financial interests.

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Wang, LP., Titov, A., McGibbon, R. et al. Discovering chemistry with an ab initio nanoreactor. Nature Chem 6, 1044–1048 (2014). https://doi.org/10.1038/nchem.2099

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