The loss landscape of powder X-ray diffraction-based structure optimization is too rough for gradient descent

2026/01/01 by Nofit Segal, Akshay Subramanian, Mingda Li +2 · 1 voice
Materials Science · Chemistry · #X-ray Diffraction in Crystallography #Machine Learning in Materials Science #Advanced NMR Techniques and Applications

paper · pdf · doi:10.1039/d6dd00017g

openalex created_date 2025/12/05 · openalex publication_date 2026/01/01 · openalex updated_date 2026/07/28

Abstract

While potential energy surfaces offer smooth convergence, we show that XRD similarity metrics create a highly non-convex, ill-posed loss landscape. This rugged topology severely complicates gradient-based crystal structure optimization.

Citations

• DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
• Powder Diffraction Crystal Structure Determination Using Generative Models
• Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data
• End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction
• A foundation model for atomistic materials chemistry
• Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
• A universal graph deep learning interatomic potential for the periodic table
• Atomic-level description of thermal fluctuations in inorganic lead halide perovskites
• Interval Estimation for a Binomial Proportion

Discussions

• 🚨New HOT article in #RSCDigital this week! Bombarelli et al explore the mapping of powder X-ray diffraction data to crystal structure, using gradient-based optimization They explore the challenges a [bsky, 1 points, 0 comments]

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