Data-driven kinetic energy density fitting for orbital-free DFT: linear vs Gaussian process regression
Sergei Manzhos; Pavlo Golub · 2020 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-free DFT (OF-DFT). We focus on the role of data distribution and on data and regressor selection. We compare unweighted and weighted linear and Gaussian process regressions of KED for light metals and a semiconductor. We find that good quality linear regression resulting in good energy-volume dependence is possible over density-dependent variables suggested in previous literature. This is achieved with weighted fitti
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Resonant amplitude distribution of the Hilda asteroids and the free-floating planet flyby scenario
Negative / Null Result Report3D Simulations of Plasma Filaments in the Scrape Off Layer: A Comparison with Models of Reduced Dimensionality
Negative / Null Result ReportHerd Behaviour in Public Goods Games
Negative / Null Result ReportAccurate coarse-graining of small organic molecules in melts and thin films using density-dependent potentials
Negative / Null Result ReportSemi-Analytical Models for Lensing by Dark Halos: I. Splitting Angles
Negative / Null Result ReportSimulated Annealing with Tsallis Weights - A Numerical Comparison
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: arXiv