Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F25%3A00637300" target="_blank" >RIV/61388955:_____/25:00637300 - isvavai.cz</a>

Result on the web

<a href="https://hdl.handle.net/11104/0368214" target="_blank" >https://hdl.handle.net/11104/0368214</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/2632-2153/ade7ca" target="_blank" >10.1088/2632-2153/ade7ca</a>

Result language

angličtina

Original language name

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory

Original language description

Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. However, ML models are not as easily handled as analytic expressions, they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative ML of crystal cell-averaged kinetic energy densities ( tau<overline>) of several hundred materials. A previously published dataset including multiple phases of 433 unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In was used for training, including data at the equilibrium geometry as well as strained structures. A hybrid Gaussian process regression-neural network method was used to understand the type of functional dependence of tau & horbar. On the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn-Sham (KS) effective potential. Based on this analysis, an analytic model is constructed that can reproduce KS DFT energy-volume curves with sufficient accuracy (pronounced minima that are sufficiently close to the minima of the Kohn-Sham DFT-based curves and with sufficiently close curvatures) to enable structure optimizations and elastic response calculations.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10403 - Physical chemistry

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Machine Learning-Science and Technology

ISSN

2632-2153

e-ISSN

2632-2153

Volume of the periodical

6

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

035002

UT code for WoS article

001522404600001

EID of the result in the Scopus database

2-s2.0-105009707881