High Throughput Crystal Structure Classification
Source abstract
Abstract
Jess Tate, Jeffery Aguiar, Matthew Gong, Tolga Tasdizen; High Throughput Crystal Structure Classification, Microscopy and Microanalysis, Volume 26, Issue S
Only a short abstract or summary was supplied by the source.
Source: openalex_inverted_index · Quality: short
Research context
OpenAlex topics: X-ray Diffraction in Crystallography · Machine Learning in Materials Science · Crystallization and Solubility Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (8)
Show source-provided references
1. 10.1177/109434201039198910.1177/1094342010391989 ↗
2. 10.1063/1.491401610.1063/1.4914016 ↗
3. 8. Work supported through the INL Laboratory Directed Research & Development (LDRD) Program under DOE Idaho Operations Office Contract DE-AC07-05ID14517. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC. a wholly owned subsidiary of Honeywell International, Inc., for the U.S. DOE's National Nuclear Security Administration under contract DE-NA-0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the United States Government. In part, this research was conducted at the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility.
4. Indexing of powder diffraction patterns by iterative use of singular value decomposition10.1107/S0021889802019878 ↗
5. 10.1107/S160057671701135910.1107/S1600576717011359 ↗
6. 10.1021/nn504730n10.1021/nn504730n ↗
7. 10.1126/sciadv.aaw194910.1126/sciadv.aaw1949 ↗
8. Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning10.1016/j.commatsci.2019.109409 ↗