Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images
© The Author(s) 2022. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd..
Relevant to broad applied fields and natural processes, interfacial ionic hydrates have been widely studied by using ultrahigh-resolution atomic force microscopy (AFM). However, the complex relationship between the AFM signal and the investigated system makes it difficult to determine the atomic structure of such a complex system from AFM images alone. Using machine learning, we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images, including the position of each atom and the orientations of water molecules. Furthermore, it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network trained with easily available interfacial water data. Thus, this work provides an efficient and economical methodology that not only opens up avenues to determine atomic structures of more complex systems from AFM images, but may also help to interpret other scientific studies involving sophisticated experimental results.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
National science review - 10(2023), 7 vom: 30. Juli, Seite nwac282 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Tang, Binze [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Atomic force microscopy |
|---|
| Anmerkungen: |
Date Revised 16.06.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1093/nsr/nwac282 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM357686012 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM357686012 | ||
| 003 | DE-627 | ||
| 005 | 20231226073127.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/nsr/nwac282 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1192.xml |
| 035 | |a (DE-627)NLM357686012 | ||
| 035 | |a (NLM)37266561 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Tang, Binze |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images |
| 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 Revised 16.06.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Author(s) 2022. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. | ||
| 520 | |a Relevant to broad applied fields and natural processes, interfacial ionic hydrates have been widely studied by using ultrahigh-resolution atomic force microscopy (AFM). However, the complex relationship between the AFM signal and the investigated system makes it difficult to determine the atomic structure of such a complex system from AFM images alone. Using machine learning, we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images, including the position of each atom and the orientations of water molecules. Furthermore, it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network trained with easily available interfacial water data. Thus, this work provides an efficient and economical methodology that not only opens up avenues to determine atomic structures of more complex systems from AFM images, but may also help to interpret other scientific studies involving sophisticated experimental results | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a atomic force microscopy | |
| 650 | 4 | |a atomic scale structure identification | |
| 650 | 4 | |a interfacial ion hydrates | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a transfer learning | |
| 700 | 1 | |a Song, Yizhi |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Mian |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Ye |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Zhen Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Duanyun |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Limei |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t National science review |d 2014 |g 10(2023), 7 vom: 30. Juli, Seite nwac282 |w (DE-627)NLM243751931 |x 2053-714X |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2023 |g number:7 |g day:30 |g month:07 |g pages:nwac282 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/nsr/nwac282 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2023 |e 7 |b 30 |c 07 |h nwac282 | ||