PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 1, 1989Journal of Computational Chemistry7,639 citations

Optimization of parameters for semiempirical methods I. Method

View Full Paper
JSJames J. P. Stewart

Key Points

  • Develop a fast, derivative-based parameter optimization algorithm for semiempirical quantum chemical models.
  • Implemented a parameter optimization method using property derivatives with respect to adjustable parameters within the modified neglect of diatomic overlap (MNDO) framework.
  • Replaced full semiempirical calculations during parameter fitting with series expressions to compute property values.
  • Achieved a substantial computational speed increase in parameter optimization by avoiding repeated full semiempirical calculations.
  • Shifted the rate-determining step in parameterizing chemical elements from mathematical optimization mechanics to the compilation of experimental reference data.

Abstract

Abstract A new method for obtaining optimized parameters for semiempirical methods has been developed and applied to the modified neglect of diatomic overlap (MNDO) method. The method uses derivatives of calculated values for properties with respect to adjustable parameters to obtain the optimized values of parameters. The large increase in speed is a result of using a simple series expression for calculated values of properties rather than employing full semiempirical calculations. With this optimization procedure, the rate‐determining step for parameterizing elements changes from the mechanics of parameterization to the assembling of experimental reference data.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

James J. P. Stewart (1989) studied this question.

synapsesocial.com/papers/69d7c7bcae371f9756ae2be4https://doi.org/10.1002/jcc.540100208
Ask AI
Helpful
Bookmark
Share
View Full Paper