End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction

• URL: http://arxiv.org/abs/2401.03862v3
• Date: Sat, 08 Feb 2025 05:24:36 GMT
• Title: End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction
• Authors: Qingsi Lai, Fanjie Xu, Lin Yao, Zhifeng Gao, Siyuan Liu, Hongshuai Wang, Shuqi Lu, Di He, Liwei Wang, Cheng Wang, Guolin Ke,
• Abstract summary: This study introduces XtalNet, the first equivariant deep generative model for end-to-end crystal structure prediction from PXRD. XtalNet leverages PXRD as an additional condition, eliminating ambiguity and enabling the generation of complex organic structures with up to 400 atoms in the unit cell. XtalNet achieves a top-10 Match Rate of 90.2% and 79% for hMOF-100 and hMOF-400 in conditional crystal structure prediction task, respectively.
• Score: 37.563382606039006
• License:
• Abstract: Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most automated method only achieved at coarse-grained level. The more difficult and important task of fine-grained crystal structure prediction from PXRD remains unaddressed. This study introduces XtalNet, the first equivariant deep generative model for end-to-end crystal structure prediction from PXRD. Unlike previous crystal structure prediction methods that rely solely on composition, XtalNet leverages PXRD as an additional condition, eliminating ambiguity and enabling the generation of complex organic structures with up to 400 atoms in the unit cell. XtalNet comprises two modules: a Contrastive PXRD-Crystal Pretraining (CPCP) module that aligns PXRD space with crystal structure space, and a Conditional Crystal Structure Generation (CCSG) module that generates candidate crystal structures conditioned on PXRD patterns. Evaluation on two MOF datasets (hMOF-100 and hMOF-400) demonstrates XtalNet's effectiveness. XtalNet achieves a top-10 Match Rate of 90.2% and 79% for hMOF-100 and hMOF-400 in conditional crystal structure prediction task, respectively. XtalNet enables the direct prediction of crystal structures from experimental measurements, eliminating the need for manual intervention and external databases. This opens up new possibilities for automated crystal structure determination and the accelerated discovery of novel materials.

Related papers

• deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models [1.231476564107544]
  We introduce an autoregressive language model that performs crystal structure prediction (CSP) from powder diffraction data. The presented model, deCIFer, generates crystal structures in the widely used Crystallographic Information File (CIF) format. We train deCIFer on nearly 2.3M unique crystal structures and validate on diverse sets of PXRD patterns for characterizing challenging inorganic crystal systems.
  arXiv  Detail & Related papers  (2025-02-04T10:09:47Z)
• CrystalX: Ultra-Precision Crystal Structure Resolution and Error Correction Using Deep Learning [44.02107704258412]
  We develop CrystalX, a model for ultra-precise structural analysis at the full-atom level. To validate the model, we employed over 50,000 X-ray diffraction measurements. This deep learning model revolutionizes the time frame for crystal structure analysis, slashing it down to seconds.
  arXiv  Detail & Related papers  (2024-10-17T16:12:55Z)
• Ab Initio Structure Solutions from Nanocrystalline Powder Diffraction Data [4.463003012243322]
  A major challenge in materials science is the determination of the structure of nanometer sized objects. We present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229 known structures. We find that our model, PXRDnet, can successfully solve simulated nanocrystals as small as 10 angstroms across 200 materials of varying symmetry and complexity.
  arXiv  Detail & Related papers  (2024-06-16T03:45:03Z)
• Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction [62.36797874900395]
  In computational chemistry, crystal structure prediction is an optimization problem. One approach to tackle this problem involves building simulators based on density functional theory (DFT) followed by running search in simulation. We show that our approach, dubbed LCOMs (latent conservative objective models), performs comparably to the best current approaches in terms of success rate of structure prediction.
  arXiv  Detail & Related papers  (2023-10-16T04:35:44Z)
• Crystal-GFN: sampling crystals with desirable properties and constraints [103.79058968784163]
  We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials. In this paper, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
  arXiv  Detail & Related papers  (2023-10-07T21:36:55Z)
• Shotgun crystal structure prediction using machine-learned formation energies [3.2563787689949133]
  Crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Here, we present significant progress toward solving the crystal structure prediction problem. We performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor.
  arXiv  Detail & Related papers  (2023-05-03T14:46:16Z)
• Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms [0.6906005491572401]
  Machine learning techniques have successfully been used to extract structural information from powder X-ray diffractograms. We propose an alternative approach of generating synthetic crystals with random coordinates by using the symmetry operations of each space group. We demonstrate online training of deep ResNet-like models on up to a few million unique on-the-fly generated synthetic diffractograms per hour.
  arXiv  Detail & Related papers  (2023-03-21T09:37:29Z)
• Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data [137.47124933818066]
  We propose an automated pipeline for the analysis of X-ray diffraction images based on the Faster R-CNN deep learning architecture. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications.
  arXiv  Detail & Related papers  (2022-02-22T15:39:00Z)
• Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns [48.53244254413104]
  We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images. We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures. Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
  arXiv  Detail & Related papers  (2022-02-01T03:53:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.