|∆ <scp> <i>μ</i> </scp> | Big Is “Not Bad”? A Quantitative Assessment

ABSTRACT We perform a thorough test of the “ big is good” principle (DMB) using double‐exchange reactions between diatomic molecules. We conclude that DMB is “not bad” at predicting whether or not a reaction will be favorable, but its performance is inferior to alternatives like the maximum hardness principle.

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Publication Details

Journal
International Journal of Quantum Chemistry
Published
2026-07-01
DOI
https://doi.org/10.1002/qua.70258
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

|∆ μ | Big Is “Not Bad”? A Quantitative Assessment

Farnaz Heidar‐Zadeh, Paul W. Ayers, Ramón Alain Miranda‐Quintana, Abdul Rahman Al Nabulsi
International Journal of Quantum Chemistry
Machine Learning in Materials Science
article

|∆ μ | Big Is “Not Bad”? A Quantitative Assessment

Farnaz Heidar‐Zadeh, Paul W. Ayers, Ramón Alain Miranda‐Quintana, Abdul Rahman Al Nabulsi
article en

Abstract

ABSTRACT We perform a thorough test of the “ big is good” principle (DMB) using double‐exchange reactions between diatomic molecules. We conclude that DMB is “not bad” at predicting whether or not a reaction will be favorable, but its performance is inferior to alternatives like the maximum hardness principle.

International Journal of Quantum ChemistryVol. 126(13)
Queen's University (CA), University of Florida (US), McMaster University (CA)
National Science Foundation, Canada Research Chairs, Queen's University, Alliance de recherche numérique du Canada, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 16%
Machine Learning in Materials Science
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