# Probabilistic Phase Labeling and Lattice Refinement for Autonomous Material Research

arXiv: https://arxiv.org/abs/2308.07897 · HTML: https://mlsanity.com/paper/2308.07897/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 79 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Autonomous synthesis of metastable materials](https://mlsanity.com/paper/2101.07385/) (2021)
- [A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra](https://mlsanity.com/paper/2103.16664/) (2021)
- [Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning](https://mlsanity.com/paper/2108.09523/) (2021)

## Built on by (most cited first, 3 of 3)

- [opXRD: Open Experimental Powder X-ray Diffraction Database](https://mlsanity.com/paper/2503.05577/) (2025)
- [AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis](https://mlsanity.com/paper/2609.00070/) (2026)
- [Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning](https://mlsanity.com/paper/2601.08185/) (2026)

## Abstract (arXiv)

X-ray diffraction (XRD) is an essential technique to determine a material's crystal structure in high-throughput experimentation, and has recently been incorporated in artificially intelligent agents in autonomous scientific discovery processes. However, rapid, automated and reliable analysis method of XRD data matching the incoming data rate remains a major challenge. To address these issues, we present CrystalShift, an efficient algorithm for probabilistic XRD phase labeling that employs symmetry-constrained pseudo-refinement optimization, best-first tree search, and Bayesian model comparison to estimate probabilities for phase combinations without requiring 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-mapping, 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.