Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data

TL;DR

This paper introduces PXRDnet, a machine learning model that accurately determines nanocrystal structures from powder diffraction data, even with limited information and noisy experimental patterns, advancing materials science.

Contribution

The paper presents a novel diffusion-based generative model trained on extensive known structures to solve nanocrystal structures from powder diffraction data, including real-world noisy patterns.

Findings

Successfully solves simulated nanocrystals as small as 10 Å across 200 materials

Achieves structural candidate verification with 80% success rate

Average error among candidates is only 7% after refinement

Abstract

A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229 known structures. The model factors both the measured diffraction pattern as well as relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-size broadened powder diffraction pattern, we find that our model, PXRDnet, can successfully solve simulated nanocrystals as small as 10 angstroms across 200 materials of varying symmetry and complexity, including structures from all seven crystal systems. We show that our model can successfully and verifiably determine structural candidates four out of five times, with average error among these candidates being only 7% (as measured by post-Rietveld refinement R-factor). Furthermore, PXRDnet is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nano-materials.