التفاصيل البيبلوغرافية
| العنوان: |
Predicting hydration layers on surfaces using deep learning |
| المؤلفون: |
Yashasvi S. Ranawat, Ygor M. Jaques, Adam S. Foster |
| المساهمون: |
Department of Applied Physics, Surfaces and Interfaces at the Nanoscale, Aalto-yliopisto, Aalto University |
| المصدر: |
Nanoscale Advances. 3:3447-3453 |
| بيانات النشر: |
Royal Society of Chemistry (RSC), 2021. |
| سنة النشر: |
2021 |
| مصطلحات موضوعية: |
Work (thermodynamics), Materials science, business.industry, Interface (Java), Deep learning, Resolution (electron density), General Engineering, Bioengineering, Nanotechnology, 02 engineering and technology, General Chemistry, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, Atomic and Molecular Physics, and Optics, 0104 chemical sciences, Natural processes, General Materials Science, Water density, Artificial intelligence, 0210 nano-technology, business, Nanoscopic scale, Biomineralization |
| الوصف: |
Funding Information: This work was supported by World Premier International Research Center Initiative (WPI), MEXT, Japan and by the Academy of Finland (project no. 314862). We are grateful to Ondˇrej Krejˇćı for careful reading of the manuscript. We also acknowledge the computational resources provided by the Aalto Science-IT project. Publisher Copyright: © The Royal Society of Chemistry 2021. Characterisation of the nanoscale interface formed between minerals and water is essential to the understanding of natural processes, such as biomineralization, and to develop new technologies where function is dominated by the mineral-water interface. Atomic force microscopy offers the potential to characterize solid-liquid interfaces in high-resolution, with several experimental and theoretical studies offering molecular scale resolution by linking measurements directly to water density on the surface. However, the theoretical techniques used to interpret such results are computationally intensive and development of the approach has been limited by interpretation challenges. In this work, we develop a deep learning architecture to learn the solid-liquid interface of polymorphs of calcium carbonate, allowing for the rapid predictions of density profiles with reasonable accuracy. |
| وصف الملف: |
application/pdf |
| تدمد: |
2516-0230 |
| URL الوصول: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac0f9df46ed3cac1c0cb3a85e66d43ef
https://doi.org/10.1039/d1na00253h |
| حقوق: |
OPEN |
| رقم الأكسشن: |
edsair.doi.dedup.....ac0f9df46ed3cac1c0cb3a85e66d43ef |
| قاعدة البيانات: |
OpenAIRE |
