Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

First published:

Last Edited:

Number of edits:

Finding new compounds and their crystal structures is an essential step to new materials discoveries. We demonstrate how this search can be accelerated using a combination of machine learning techniques and high-throughput ab initio computations. Using a probabilistic model built on an experimental crystal structure database, novel compositions that are most likely to form a compound, and their most-probable crystal structures, are identified and tested for stability by ab initio computations. We performed such a large-scale search for new ternary oxides, discovering 209 new compounds with a limited computational budget. A list of these predicted compounds is provided, and we discuss the chemistries in which high discovery rates can be expected.

  • Source:
  • Tags:

Backlinks

These are the other notes that link to this one.

Nothing links here, how did you reach this page then?

Comment

Share your thoughts on this note. Comments are not public, they are messages sent directly to my inbox.
Aquiles Carattino
Aquiles Carattino
This note you are reading is part of my digital garden. Follow the links to learn more, and remember that these notes evolve over time. After all, this website is not a blog.
© 2024 Aquiles Carattino
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
Privacy Policy