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<p>X-ray diffraction (XRD) is a powerful method for determining a material’s crystal structure in high-throughput experimentation, and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery. However, rapid, automated, and reliable analysis of XRD data at rates that match the pace of experimental measurements at a synchrotron source remains a major challenge. To address these issues, we developed CrystalShift for rapid and efficient probabilistic XRD phase labeling employing symmetry-constrained optimization, best-first tree search, and Bayesian model comparison. The algorithm estimates probabilities for phase combinations without requiring additional phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase labeling, CrystalShift offers quantitative insights into materials’ structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery.</p>

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Probabilistic phase labeling and lattice refinement for autonomous materials research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

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  • Ming-Chiang Chang,
  • Sebastian Ament,
  • Maximilian Amsler,
  • Duncan R. Sutherland,
  • Lan Zhou,
  • John M. Gregoire,
  • Carla P. Gomes,
  • R. Bruce van Dover,
  • Michael O. Thompson

摘要

X-ray diffraction (XRD) is a powerful method for determining a material’s crystal structure in high-throughput experimentation, and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery. However, rapid, automated, and reliable analysis of XRD data at rates that match the pace of experimental measurements at a synchrotron source remains a major challenge. To address these issues, we developed CrystalShift for rapid and efficient probabilistic XRD phase labeling employing symmetry-constrained optimization, best-first tree search, and Bayesian model comparison. The algorithm estimates probabilities for phase combinations without requiring additional phase space information or training. We demonstrate that CrystalShift provides robust probability estimates, outperforming existing methods on synthetic and experimental datasets, and can be readily integrated into high-throughput experimental workflows. In addition to efficient phase labeling, CrystalShift offers quantitative insights into materials’ structural parameters, which facilitate both expert evaluation and AI-based modeling of the phase space, ultimately accelerating materials identification and discovery.