MAXDIS: Difference between revisions
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At each ionic step, the maximum Cartesian displacement of any atom (using minimum-image convention) is compared to MAXDIS. If exceeded, the charge density extrapolation controlled by IWAVPR is skipped and atomic densities are used instead. Setting {{TAG|MAXDIS}}=0.0 disables this reset entirely. | At each ionic step, the maximum Cartesian displacement of any atom (using minimum-image convention) is compared to MAXDIS. If exceeded, the charge density extrapolation controlled by {{TAG|IWAVPR}} is skipped and atomic densities are used instead. Setting {{TAG|MAXDIS}}=0.0 disables this reset entirely. | ||
{{TAG|MAXDIS}} is particularly important in [[:Category:Machine-learned force fields|on-the-fly machine learning force field]] ({{TAG|ML_MODE}}=train) calculations, where many machine-learning-driven ionic steps are executed between successive ab initio evaluations. This allows atoms to travel a considerable distance between two DFT calculations, making the charge density from the previous ab initio step a poor initial guess for the next one — potentially causing slow or problematic electronic convergence. | |||
== Related tags and articles == | == Related tags and articles == | ||
{{TAG| | {{TAG|IWAVPR}}, {{TAG|IBRION}}, {{TAG|POTIM}}, {{TAG|ML_LMLFF}}, {{TAG|ML_MODE}} | ||
[[Category:INCAR tag]] | [[Category:INCAR tag]] | ||
Latest revision as of 08:50, 10 June 2026
MAXDIS = [real]
| Default: MAXDIS | = 0.0 (switched off) |
Description: This tag sets the maximum distance that an atom is allowed to travel (in Angstrom) between two ab-initio steps before the charge density is reset to atomic an atomic charge density.
At each ionic step, the maximum Cartesian displacement of any atom (using minimum-image convention) is compared to MAXDIS. If exceeded, the charge density extrapolation controlled by IWAVPR is skipped and atomic densities are used instead. Setting MAXDIS=0.0 disables this reset entirely.
MAXDIS is particularly important in on-the-fly machine learning force field (ML_MODE=train) calculations, where many machine-learning-driven ionic steps are executed between successive ab initio evaluations. This allows atoms to travel a considerable distance between two DFT calculations, making the charge density from the previous ab initio step a poor initial guess for the next one — potentially causing slow or problematic electronic convergence.