Systems and methods for materials characterisation
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Solution Overview
Problem
Automated Rietveld refinement for characterizing crystalline materials is complex due to the complexity of fitting multiple parameters, such as background, atomic occupancy, strain, peak shape, temperature, sample orientation, and lattice parameter, making it difficult to automate and optimize.
Innovation Solution
A modified Rietveld refinement method that incorporates a likelihood estimator based on a predicted phase distribution, calculated using synthesis information, to bias the goodness-of-fit towards models that are more likely to exist under reaction conditions, thereby improving the accuracy of structural modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Rietveld refinement is used to fit multiple parameters (background, atomic occupancy, strain, peak shape, temperature, sample orientation, lattice parameter), then the structural characterization accuracy is improved, but the complexity of automation and optimization deteriorates
Solution Approach 1:
The patent applies preliminary action by using synthesis information (reaction conditions, precursor compositions, thermodynamic stability) to predict the phase distribution before performing Rietveld refinement. This predicted phase distribution is then used to constrain and guide the refinement process, reducing the search space for multiple parameters and making automation feasible while maintaining structural characterization accuracy
2Measurement precision
If traditional Rietveld refinement minimizes only the R-factor based on diffraction pattern fit, then the goodness-of-fit to diffraction data is improved, but the plausibility of structural models under reaction conditions deteriorates
Solution Approach 1:
The patent implements feedback by incorporating a likelihood estimator that compares the predicted phase distribution (from synthesis information) with the measured phase distribution (from Rietveld refinement). This likelihood estimator provides feedback to the optimization process, penalizing models that fit the diffraction pattern well but are implausible under the given reaction conditions, thus ensuring both goodness-of-fit and chemical plausibility
3Measurement precision
If expert input is required to determine the order of fitting parameters, then the quality of structural characterization is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent applies self-service by enabling the Rietveld refinement system to automatically determine the optimal fitting sequence and parameters using the predicted phase distribution from synthesis information. The likelihood estimator automatically guides the refinement process without requiring expert intervention to specify the order of fitting parameters, making the system easier to operate and automate while maintaining high quality structural characterization
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and automation of Rietveld refinement by ensuring that the structural model selected is not only a good fit to the diffraction pattern but also plausible based on the reaction conditions, leading to improved characterization of crystalline materials.
Implementation Method 1
Most new materials are structurally characterised using X-ray diffraction (X-ray diffraction)
Implementation Method 2
obtaining diffraction data from diffraction of the crystalline material
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
A computer implemented method (100) of characterising a crystalline material is disclosed. The method (100) includes obtaining diffraction data (101) and a predicted phase distribution (11) from a crystalline material. Modified Rietveld refinement is performed on the diffraction data. This includes i) determining an R-weighted pattern value, Rwp, and an R-expected value, Re , for a structural model of the crystalline material and ii) determining (104, 107) a goodness-of-fit value based on Rwp , Re and a likelihood estimator for the structural model. The goodness-of-fit value is optimised (108) by repeating steps i) and ii) for different structural models of the crystalline material. An optimised structural model that provides an optimised goodness-of-fit value is obtained and the method outputs (109) a characterisation of the crystalline material based on the obtained optimised structural model. The likelihood estimator is based on the predicted phase distribution (11) of the crystalline material and a measured phase distribution (105), which is based on the structural model.