تقرير
Diffusion Models Are Promising for Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data
| العنوان: | Diffusion Models Are Promising for Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data |
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| المؤلفون: | Guo, Gabe, Saidi, Tristan, Terban, Maxwell, Billinge, Simon JL, Lipson, Hod |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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 a Diffusion model 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 determine structural solutions with up to $81.5\%$ accuracy, as measured by structural correlation. 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.10796 |
| رقم الأكسشن: | edsarx.2406.10796 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |