4.5 Article

Systematic Improvement of a Classical Molecular Model of Water

期刊

JOURNAL OF PHYSICAL CHEMISTRY B
卷 117, 期 34, 页码 9956-9972

出版社

AMER CHEMICAL SOC
DOI: 10.1021/jp403802c

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资金

  1. SimBios program
  2. NIH [U54 GM072970]
  3. Department of Defense (Office of the Director of Defense Research and Engineering) through the National Security Science and Engineering Fellowship program
  4. NSF [CHE-1265731, CHE-1048789]
  5. National Science Foundation Division of Chemistry [NSF CHE-1152823]
  6. Robert A. Welch Foundation [F-1691]
  7. National Science Foundation [OCI-1053575, OCI-0910735]
  8. Stampede supercomputer under XSEDE grant [MCB100057]
  9. American Recovery and Reinvestment Act [111-5]
  10. Direct For Mathematical & Physical Scien
  11. Division Of Chemistry [1265712] Funding Source: National Science Foundation
  12. Division Of Chemistry
  13. Direct For Mathematical & Physical Scien [1048789, 1152823, 1265731] Funding Source: National Science Foundation

向作者/读者索取更多资源

We report the iAMOEBA (inexpensive AMOEBA) classical polarizable water model. The iAMOEBA model uses a direct approximation to describe electronic polarizability, in which the induced dipoles are determined directly from the permanent multipole electric fields and do not interact with one another. The direct approximation reduces the computational cost relative to a fully self-consistent polarizable model such as AMOEBA. The model is parameterized using ForceBalance, a systematic optimization method that simultaneously utilizes training data from experimental measurements and high-level ab initio calculations. We show that iAMOEBA is a highly accurate model for water in the solid, liquid, and gas phases, with the ability to fully capture the effects of electronic polarization and predict a comprehensive set of water properties beyond the training data set including the phase diagram. The increased accuracy of AMOEBA over the fully polarizable AMOEBA model demonstrates ForceBalance as a method that allows the researcher to systematically improve empirical models by efficiently utilizing the available data.

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