Ensemble machine learning methods: predicting electron stopping powers from a small experimental database-Reference-Cited by-同舟云学术

Ensemble machine learning methods: predicting electron stopping powers from a small experimental database

Published:2021 Issue:10 Volume:23 Page:6062-6074
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Mehnaz ¹²³,Yang L. H.¹²³,Da B.⁴⁵⁶,Ding Z. J.¹²³^ORCID

Affiliation:

1. Hefei National Laboratory for Physical Sciences at Microscale and Department of Physics, University of Science and Technology of China

2. Hefei

3. P. R. China

4. Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba

5. Ibaraki 305-0047

6. Japan

Abstract

With a small database of electron SPs and basic material properties, the ensemble ML technique enabled to predict SPs for the elements for which no data exist as well as to extrapolate SPs to lower energies where other theoretical approaches failed.

Funder

National Key Research and Development Program of China

Higher Education Discipline Innovation Project

Sumitomo Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D0CP06521H

Reference79 articles.

1. Monte Carlo backscattering yield (BY) calculations applying continuous slowing down approximation (CSDA) and experimental data