What question did this study set out to answer?

Develop a fast, derivative-based parameter optimization algorithm for semiempirical quantum chemical models.

March 1, 1989Journal of Computational Chemistry

Optimization of parameters for semiempirical methods I. Method

View Full Paper

Ask AI

Bookmark

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.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Computational study demonstrates rapid parameter optimization in semiempirical quantum chemistry models, indicating parameterization bottlenecks shift toward reference data collection.

Cite This Study

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

synapsesocial.com/papers/69d7c7bcae371f9756ae2be4 https://doi.org/10.1002/jcc.540100208

View Full Paper

Ask AI

Bookmark

Implication

Computational study demonstrates rapid parameter optimization in semiempirical quantum chemistry models, indicating parameterization bottlenecks shift toward reference data collection.

Optimization of parameters for semiempirical methods I. Method

Key Points

Discussion

Implication

Cite This Study

Optimization of parameters for semiempirical methods I. Method

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider