In crystallographic texture analysis, ensuring that sample directions are preserved from experiment to the resulting orientation distribution is crucial to obtain physical meaning from diffraction data. This work details a procedure to ensure instrument and sample coordinates are consistent when analyzing diffraction data with a Rietveld refinement using the texture analysis software
This content will become publicly available on March 1, 2025
Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis
Materials discovery lays the foundation for many technological advancements. The prediction and
discovery of new materials are not simple tasks. Here, we outline some basic principles of solid-state
chemistry, which might help to advance both, and discuss pitfalls and challenges in materials discovery.
Using the recent work of Szymanski et al. [Nature 624, 86 (2023)], which reported the autonomous discovery of 43 novel materials, as an example, we discuss problems that can arise in unsupervised materials
discovery and hope that by addressing these, autonomous materials discovery can be brought closer to
reality. We discuss all 43 synthetic products and point out four common shortfalls in the analysis. These
errors unfortunately lead to the conclusion that no new materials have been discovered in that work. We
conclude that there are two important points of improvement that require future work from the community, as follows. (i) Automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. Future
improvement of such, and the development of a reliable artificial-intelligence-based tool for Rietveld fitting, would be very helpful, not only for autonomous materials discovery but also for the community in
general. (ii) We find that disorder in materials is often neglected in predictions. The predicted compounds
investigated herein have all their elemental components located on distinct crystallographic positions but
in reality, elements can share crystallographic sites, resulting in higher-symmetry space groups and—very
often—known alloys or solid solutions. This error might be related to the difficulty of modeling disorder
in a computationally economical way and needs to be addressed both by computational and experimental material scientists. We find that two thirds of the claimed successful materials in Szymanski et al. are
likely to be known compositionally disordered versions of the predicted ordered compounds. We highlight
important issues in materials discovery, computational chemistry, and autonomous interpretation of x-ray
diffraction. We discuss concepts of materials discovery from an experimentalist point of view, which we
hope will be helpful for the community to further advance this important new aspect of our field.
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- Award ID(s):
- 2118310
- PAR ID:
- 10532478
- Publisher / Repository:
- PRX ENERGY
- Date Published:
- Journal Name:
- PRX Energy
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2768-5608
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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