Machine Learning to Predict Quasicrystals from Chemical Compositions (Adv. Mater. 36/2021)
Source abstract
Abstract
Quasicrystals In article number 2102507, Kaoru Kimura, Ryo Yoshida, and co-workers demonstrate that machine-learning algorithms can predict the chemical composition of new quasicrystals. Furthermore, analyzing the input–output relationships black-boxed into the machine-learning model, they successfully identify nontrivial empirical equations interpretable by humans that describe the conditions necessary for stable quasicrystal formation. This is the first step toward understanding the formation mechanism of quasicrystals, which has been long sought in quasicrystal research.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Quasicrystal Structures and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa 190‐8562 Japan
Department of Advanced Materials Science The University of Tokyo Kashiwa 277‐8561 Japan
Department of Materials Science and Technology Tokyo University of Science Tokyo 125–8585 Japan
Research and Service Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305‐0047 Japan
Department of Statistical Science The Graduate University for Advanced Studies Tachikawa 190‐8562 Japan
References
Reference metadata was not supplied for this record.