|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM351416889 |
| 003 |
DE-627 |
| 005 |
20231226051320.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2023 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1002/adma.202208220
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1171.xml
|
| 035 |
|
|
|a (DE-627)NLM351416889
|
| 035 |
|
|
|a (NLM)36630711
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Kløve, Magnus
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a A Machine-Learning-Based Approach for Solving Atomic Structures of Nanomaterials Combining Pair Distribution Functions with Density Functional Theory
|
| 264 |
|
1 |
|c 2023
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 29.03.2023
|
| 500 |
|
|
|a Date Revised 29.03.2023
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a © 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.
|
| 520 |
|
|
|a Determination of crystal structures of nanocrystalline or amorphous compounds is a great challenge in solid-state chemistry and physics. Pair distribution function (PDF) analysis of X-ray or neutron total scattering data has proven to be a key element in tackling this challenge. However, in most cases, a reliable structural motif is needed as a starting configuration for structure refinements. Here, an algorithm that is able to determine the crystal structure of an unknown compound by means of an on-the-fly trained machine learning model, which combines density functional theory calculations with comparison of calculated and measured PDFs for global optimization in an artificial landscape, is presented. Due to the nature of this landscape, even metastable configurations and stacking disorders can be identified
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a crystal structure prediction
|
| 650 |
|
4 |
|a density functional theory
|
| 650 |
|
4 |
|a global optimization
|
| 650 |
|
4 |
|a machine learning
|
| 650 |
|
4 |
|a nanomaterials
|
| 650 |
|
4 |
|a pair distribution function
|
| 700 |
1 |
|
|a Sommer, Sanna
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Iversen, Bo B
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Hammer, Bjørk
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Dononelli, Wilke
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 35(2023), 13 vom: 01. März, Seite e2208220
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
|
| 773 |
1 |
8 |
|g volume:35
|g year:2023
|g number:13
|g day:01
|g month:03
|g pages:e2208220
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1002/adma.202208220
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 35
|j 2023
|e 13
|b 01
|c 03
|h e2208220
|