Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data
This addresses a major challenge in materials science for researchers needing to determine structures of nanometer-sized objects, representing a novel method for a known bottleneck.
The paper tackled the problem of determining atomic structures of nanocrystals from limited powder diffraction data by developing PXRDnet, a generative machine learning model based on diffusion processes, which successfully solved simulated nanocrystals as small as 10 angstroms across 200 materials with a 80% success rate and 7% average error.
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.