1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
f
Communication: Energy benchmarking with quantum Monte Carlo for water nano-droplets and bulk liquid water
Rent:
Rent this article for
Access full text Article
/content/aip/journal/jcp/138/22/10.1063/1.4810882
1.
1. H. Popkie, H. Kistenmacher, and E. Clementi, J. Chem. Phys. 59, 1325 (1973).
http://dx.doi.org/10.1063/1.1680187
2.
2. K. Laasonen, M. Sprik, M. Parrinello, and R. Car, J. Chem. Phys. 99, 9080 (1993).
http://dx.doi.org/10.1063/1.465574
3.
3. R. Bukowski, K. Szalewicz, G. C. Groenenboom, and A. van der Avoird, Science 315, 1249 (2007).
http://dx.doi.org/10.1126/science.1136371
4.
4. Y. Wang, X. Huang, B. C. Shepler, B. J. Braams, and J. M. Bowman, J. Chem. Phys. 134, 094509 (2011).
http://dx.doi.org/10.1063/1.3554905
5.
5. V. Babin, G. R. Medders, and F. Paesani, J. Phys. Chem. Lett. 3, 3765 (2012).
http://dx.doi.org/10.1021/jz3017733
6.
6. W. Klopper, J. G. C. M. van Duijenveldt-van de Rijdt, and F. B. van Duijenveldt, Phys. Chem. Chem. Phys. 2, 2227 (2000).
http://dx.doi.org/10.1039/a910312k
7.
7. G. S. Tschumper, M. L. Leininger, B. C. Hoffman, E. F. Valeev, H. F. Schaefer, and M. Quack, J. Chem. Phys. 116, 690 (2002).
http://dx.doi.org/10.1063/1.1408302
8.
8. D. M. Bates and G. S. Tschumper, J. Phys. Chem. A 113, 3555 (2009).
http://dx.doi.org/10.1021/jp8105919
9.
9. S. Yoo, A. Aprà, X. C. Xeng, and S. S. Xantheas, J. Phys. Chem. Lett. 1, 3122 (2010).
http://dx.doi.org/10.1021/jz101245s
10.
10. U. Góra, R. Podeszwa, W. Cencek, and K. Szalewicz, J. Chem. Phys. 135, 224102 (2011).
http://dx.doi.org/10.1063/1.3664730
11.
11. B. Santra, J. Klimeš, D. Alfè, A. Tkatchenko, B. Slater, A. Michaelides, R. Car, and M. Scheffler, Phys. Rev. Lett. 107, 185701 (2011).
http://dx.doi.org/10.1103/PhysRevLett.107.185701
12.
12. P. J. Bygrave, N. L. Allan, and F. R. Manby, J. Chem. Phys. 137, 164102 (2012).
http://dx.doi.org/10.1063/1.4759079
13.
13. W. M. C. Foulkes, L. Mitaš, R. J. Needs, and G. Rajagopal, Rev. Mod. Phys. 73, 33 (2001).
http://dx.doi.org/10.1103/RevModPhys.73.33
14.
14. R. J. Needs, M. D. Towler, N. D. Drummond, and P. López-Ríos, J. Phys. Condens. Matter 22, 023201 (2010).
http://dx.doi.org/10.1088/0953-8984/22/2/023201
15.
15.See supplementary material at http://dx.doi.org/10.1063/1.4810882 for computational details. [Supplementary Material]
16.
16. J. C. Grossman, E. Schwegler, E. W. Draeger, F. Gygi, and G. Galli, J. Chem. Phys. 120, 300 (2004).
http://dx.doi.org/10.1063/1.1630560
17.
17. A. D. Becke, Phys. Rev. A 38, 3098 (1988);
http://dx.doi.org/10.1103/PhysRevA.38.3098
17.C. Lee, W. Yang, and R. Parr, Phys. Rev. B 37, 785 (1988).
http://dx.doi.org/10.1103/PhysRevB.37.785
18.
18. B. Santra, A. Michaelides, and M. Scheffler, J. Chem. Phys. 127, 184104 (2007).
http://dx.doi.org/10.1063/1.2790009
19.
19. B. Santra, A. Michaelides, M. Fuchs, A. Tkatchenko, C. Filippi, and M. Scheffler, J. Chem. Phys. 129, 194111 (2008).
http://dx.doi.org/10.1063/1.3012573
20.
20. O. Kambara, K. Takahashi, M. Hayashi, and J. L. Kuo, Phys. Chem. Chem. Phys. 14, 11484 (2012).
http://dx.doi.org/10.1039/c2cp41495c
21.
21. H.-S. Lee and M. E. Tuckerman, J. Chem. Phys. 125, 154507 (2006).
http://dx.doi.org/10.1063/1.2354158
22.
22. S. Yoo, X. C. Xeng, and S. S. Xantheas, J. Chem. Phys. 130, 221102 (2009).
http://dx.doi.org/10.1063/1.3153871
23.
23. J. Schmidt, J. VandeVondele, I.-F. W. Kuo, D. Sebastiani, J. I. Siepmann, J. Hutter, and C. J. Mundy, J. Phys. Chem. B 113, 11959 (2009).
http://dx.doi.org/10.1021/jp901990u
24.
24. J. Wang, G. Román-Pérez, J. M. Soler, E. Artacho, and M.-V. Fernández-Serra, J. Chem. Phys. 134, 024516 (2011).
http://dx.doi.org/10.1063/1.3521268
25.
25. Z. Ma, Y. Zhang, and M. E. Tuckerman, J. Chem. Phys. 137, 044506 (2012).
http://dx.doi.org/10.1063/1.4736712
26.
26. J. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996).
http://dx.doi.org/10.1103/PhysRevLett.77.3865
27.
27. P. H.-L. Sit and N. Marzari, J. Chem. Phys. 122, 204510 (2005).
http://dx.doi.org/10.1063/1.1908913
28.
28. S. S. Xantheas, J. Chem. Phys. 100, 7523 (1994).
http://dx.doi.org/10.1063/1.466846
29.
29. J. M. Pedulla, F. Vila, and K. D. Jordan, J. Chem. Phys. 105, 11091 (1996).
http://dx.doi.org/10.1063/1.472910
30.
30. C. A. Coulson and D. Eisenberg, Proc. R. Soc. London, Ser. A 291, 445 (1966).
http://dx.doi.org/10.1098/rspa.1966.0105
31.
31. P. L. Silvestrelli and M. Parrinello, Phys. Rev. Lett. 82, 3308 (1999).
http://dx.doi.org/10.1103/PhysRevLett.82.3308
32.
32. M. J. Gillan, F. R. Manby, M. D. Towler, and D. Alfè, J. Chem. Phys. 136, 244105 (2012).
http://dx.doi.org/10.1063/1.4730035
33.
33. G. R. Medders, V. Babin, and F. Paesani, J. Chem. Theory Comput. 9, 1103 (2013).
http://dx.doi.org/10.1021/ct300913g
34.
34. H. R. Leverentz, H. W. Qi, and D. G. Truhlar, J. Chem. Theory Comput. 9, 995 (2013).
http://dx.doi.org/10.1021/ct300848z
35.
35. I. G. Gurtubay and R. J. Needs, J. Chem. Phys. 127, 124306 (2007).
http://dx.doi.org/10.1063/1.2770711
36.
36. D. Alfè and M. J. Gillan, Phys. Rev. B 70, 161101(R) (2004).
http://dx.doi.org/10.1103/PhysRevB.70.161101
37.
37. A. Bartók, M. C. Payne, R. Kondor, and G. Csányi, Phys. Rev. Lett. 104, 136403 (2010).
http://dx.doi.org/10.1103/PhysRevLett.104.136403
38.
38. A. P. Bartók, M. J. Gillan, F. R. Manby, and G. Csányi, “Machine learning for predictive condensed-phase simulation,” Phys. Rev. B (submitted) [preprint arXiv:1302.5680].
39.
39. M. J. Gillan, D. Alfè, A. P. Bartók, and G. Csányi, “First-principles energetics of water: A many-body analysis,” Phys. Rev. B (submitted) [preprint arXiv:1303.0751].
40.
40. G. S. Fanourgakis and S. S. Xantheas, J. Chem. Phys. 128, 074506 (2008).
http://dx.doi.org/10.1063/1.2837299
41.
41. L. B. Skinner, C. J. Benmore, J. Neuefeind, and J. B. Parise, “A structural description of the compressibility minimum in water” (unpublished).
42.
42.See www.homepages.ucl.ac.uk/ucfbdxa/qmc.htm for atomic coordinates and DMC benchmark energies of the nano-droplet and liquid-state configurations studied in this work.
http://aip.metastore.ingenta.com/content/aip/journal/jcp/138/22/10.1063/1.4810882
Loading
View: Figures

Figures

Image of FIG. 1.

Click to view

FIG. 1.

Energy errors of BLYP functional after correction for 1-body (BLYP-1), 1- and 2-body (BLYP-2) and 1-, 2-, and 3-body (BLYP-3) errors for thermal samples of 100 configurations of the (HO) (left panel) and (HO) (right panel) clusters. Errors (deviations from DMC benchmarks) are plotted against squared radius of gyration of the cluster. Configurations are from a single simulation with the TTM3-F model for the 6-mer but from two separate simulations (identified as tf and te) for the 15-mer. Units: m /monomer.

Image of FIG. 2.

Click to view

FIG. 2.

Oxygen-oxygen radial distribution function () from simulations of liquid water (64 molecules in repeating cell) at = 350 K performed with BLYP (dashed green curve) and BLYP-2 (solid red curve) approximations, compared with data from high-energy x-ray diffraction at 343 K (dotted blue curve). The BLYP and BLYP-2 simulations were performed at densities 0.778 and 1.049 g/cm, respectively (see text).

Image of FIG. 3.

Click to view

FIG. 3.

Energy errors of the BLYP (red, green symbols) and BLYP-2 (blue, magenta) total-energy functions for two sets of configurations of liquid water. Errors (deviations from DMC benchmarks) are shown for 10 configurations each drawn from m.d. simulations performed near zero pressure with the BLYP (squares) and BLYP-2 (circles) total-energy functions.

Loading

Article metrics loading...

/content/aip/journal/jcp/138/22/10.1063/1.4810882
2013-06-12
2014-04-21

Abstract

We show the feasibility of using quantum Monte Carlo (QMC) to compute benchmark energies for configuration samples of thermal-equilibrium water clusters and the bulk liquid containing up to 64 molecules. Evidence that the accuracy of these benchmarks approaches that of basis-set converged coupled-cluster calculations is noted. We illustrate the usefulness of the benchmarks by using them to analyze the errors of the popular BLYP approximation of density functional theory (DFT). The results indicate the possibility of using QMC as a routine tool for analyzing DFT errors for non-covalent bonding in many types of condensed-phase molecular system.

Loading

Full text loading...

/deliver/fulltext/aip/journal/jcp/138/22/1.4810882.html;jsessionid=5qs7i7j2s61pv.x-aip-live-01?itemId=/content/aip/journal/jcp/138/22/10.1063/1.4810882&mimeType=html&fmt=ahah&containerItemId=content/aip/journal/jcp
true
true
This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Communication: Energy benchmarking with quantum Monte Carlo for water nano-droplets and bulk liquid water
http://aip.metastore.ingenta.com/content/aip/journal/jcp/138/22/10.1063/1.4810882
10.1063/1.4810882
SEARCH_EXPAND_ITEM