Deep learning for visualization and novelty detection in large X-ray diffraction datasets
- We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn't know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both "on-the-fly" and during \(\textit {post hoc}\) analysis.
| Author: | Lars BankoORCiDGND, Phillip M. MaffettoneORCiDGND, Dennis NaujoksGND, Daniel OldsORCiDGND, Alfred LudwigORCiDGND |
|---|---|
| URN: | urn:nbn:de:hbz:294-99384 |
| DOI: | https://doi.org/10.1038/s41524-021-00575-9 |
| Parent Title (English): | npj computational materials |
| Publisher: | Nature Publ. Group |
| Place of publication: | London |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2023/06/02 |
| Date of first Publication: | 2021/07/09 |
| Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
| Volume: | 7 |
| Issue: | Article 104 |
| First Page: | 104-1 |
| Last Page: | 104-6 |
| Note: | Dieser Beitrag ist auf Grund des DEAL-Springer-Vertrages frei zugänglich. |
| Institutes/Facilities: | Institut für Werkstoffe |
| Institut für Werkstoffe, Materials Discovery and Interfaces | |
| Dewey Decimal Classification: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften, Maschinenbau |
| open_access (DINI-Set): | open_access |
| faculties: | Fakultät für Maschinenbau |
| Licence (English): |  Creative Commons - CC BY 4.0 - Attribution 4.0 International |
