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Abstract The nucleobase derivative 5‐aminouracil (AUr, C4H5N3O2) is of interest for its biological activity, yet the solid state structure of this compound has remained elusive owing to its propensity to crystallize as aggregates of microcrystalline particles. Here we report the first single‐crystal structure of AUr determined from synchrotron x‐ray diffraction data. An early crystal structure prediction effort, which assumed that AUr was rigid in the isolated molecule optimized conformation, provided several poor matches to the simulated PXRD pattern. Revisiting these crystal structures, by periodic electronic level modelling (PBE‐TS optimization) gave more realistic relative lattice energies, but a good match to the experimental powder pattern required using the experimental cell parameters. PXRD and Raman spectroscopy suggest that phase impurities may be present in the bulk crystallization product, though the identity of alternative polymorphs could not be confirmed on the basis of the data available.
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opXRD: Open Experimental Powder X‐Ray Diffraction Database
Powder X‐ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high‐throughput discovery in self‐driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low‐quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X‐ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, for example, through transfer learning methods. We collected 92,552 diffractograms, 2179 of them labeled, from a wide spectrum of material classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self‐driving materials labs.
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- Award ID(s):
- 2227178
- PAR ID:
- 10640257
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Discovery
- ISSN:
- 2943-9981
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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