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Updated: Mar 3, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
Published on: April 12, 2019
Response to Comment on "Density functional theory is straying from the path toward the exact functional"
Michael G Medvedev1,2, Ivan S Bushmarinov3, Jianwei Sun4
1X-ray Structural Laboratory, A. N. Nesmeyanov Institute of Organoelement Compounds RAS, 119991 Moscow, Russian Federation. medvedev.m.g@gmail.com perdew@temple.edu.
Flexible functional forms in density fitting enhance interpolation but reduce predictive power for molecular bonding. This finding holds despite concerns about atomic density relevance.
Area of Science:
- Computational chemistry
- Materials science
Background:
- Density functional theory (DFT) is crucial for predicting material properties.
- Developing accurate and predictive density functionals is an ongoing challenge.
- The choice of fitting functional forms impacts DFT accuracy.
Purpose of the Study:
- To evaluate the impact of flexible functional forms on density fitting.
- To assess the generalizability and predictive power of density functionals.
- To address criticisms regarding the relevance of atomic densities in the fitting process.
Main Methods:
- Utilizing unconstrained fitting of flexible functional forms for density fitting.
- Analyzing the interpolative versus predictive capabilities of the resulting density functionals.
- Comparing results against established methods and addressing specific critiques.
Main Results:
- Unconstrained fitting with flexible functional forms increases interpolative ability.
- This approach significantly diminishes the wide predictive power of density functionals.
- The study confirms that fitting flexibility can lead to overfitting.
Conclusions:
- While flexible fitting enhances interpolation, it compromises broad predictive accuracy in density functionals.
- The main conclusion regarding the trade-off between interpolation and prediction remains valid.
- Further research is needed to balance fitting flexibility with predictive generalization.
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