Crystal Diffraction Data Merging for Reliable Structure Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing crystal structure analysis methods face challenges in obtaining highly reliable results due to the large amount of diffraction data requiring significant data processing power, high costs, and long processing times, and simple data merging does not provide satisfactory outcomes.
Innovation Solution
A method and device for crystal structure analysis that involves irradiating samples with radiation while changing the angle of incidence, determining diffraction spot intensity and reliability, and selectively merging data based on merging criteria to reduce processing load and time, ensuring high reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all diffraction data from a large number of crystals are merged, then the completeness of diffraction data increases, but data processing power, costs, and processing times increase significantly
Solution Approach 1:
The patent extracts and removes low-quality diffraction data from the merging process by introducing a quality evaluation mechanism. Data is assessed based on completeness, R-factor, and other quality metrics, and only high-quality data is selected for merging. This extraction of poor-quality data reduces the overall data processing burden while maintaining high completeness in the final merged dataset.
Solution Approach 2:
The patent applies local quality control by evaluating and filtering data from individual crystals based on their specific quality characteristics. Each crystal's diffraction data is assessed independently using metrics such as completeness and R-factor, allowing selective inclusion of high-quality local data regions while excluding low-quality ones. This localized quality assessment optimizes the merging process by focusing computational resources on valuable data.
2Reliability
If all diffraction data from a large number of crystals are merged, then the completeness of diffraction data increases, but processing times increase significantly
Solution Approach 1:
The patent performs preliminary evaluation and filtering of diffraction data before the merging process. By pre-assessing data quality metrics such as completeness and R-factor for each crystal, the system identifies and selects only high-quality data sets for merging. This preliminary action reduces the volume of data requiring subsequent processing, thereby significantly decreasing processing time while ensuring high completeness in the final result.
Solution Approach 2:
The patent applies partial action by selectively merging only a subset of diffraction data that meets quality criteria, rather than merging all available data. The system determines an optimal number of crystals to process based on achieving sufficient completeness thresholds, avoiding unnecessary processing of excessive data that would extend processing time without providing additional benefit.
3Ease of operation
If simple data merging is performed, then processing is simplified, but the reliability of crystal structure determination is insufficient
Solution Approach 1:
The patent implements a feedback mechanism in the data merging process by continuously evaluating quality metrics such as R-factor and completeness during merging. The system monitors these metrics and adjusts the merging strategy accordingly, selecting data from additional crystals or excluding low-quality data based on real-time feedback. This feedback loop ensures high reliability of crystal structure determination while maintaining operational simplicity through automated quality control.
Solution Approach 2:
The patent changes key parameters such as completeness thresholds, R-factor limits, and data selection criteria to optimize both simplicity and reliability. By adjusting these parameters based on the specific characteristics of the diffraction data, the system achieves reliable crystal structure determination without overly complex procedures. Parameter optimization allows the merging process to adapt to different data qualities while maintaining ease of operation.
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
The method enables efficient and cost-effective analysis by reducing data processing requirements and improving the reliability of crystal structure determination through selective data merging.
Implementation Method 1
a plurality of micro regions within a sample are sequentially irradiated with an electron beam, and a plurality of diffraction profiles for the plurality of micro regions are acquired by acquiring diffraction data using electron beams diffracted in the individual micro regions
Data Source
AI summary
A crystal structure analysis in which: each of a plurality of samples are irradiated while the angle of incidence is continuously changed by rotating the crystal, whereby diffraction spot intensity and reliability for a plurality of crystal lattice planes are determined and a data set (Data-1, Data-2, Data-3, . . . , Data-n) is acquired; whether or not to perform merging, which is a process of combining multiple sets of data into one, is determined for each individual set of data on the basis of a merging criterion (e.g., Rint, completeness); merging is performed on data for which merging is to be performed; and a crystal structure is determined according to merged data. A crystal structure analysis result is obtained in a crystal structure analysis method, a crystal structure analysis device, and a crystal structure analysis program in which a crystal structure is determined by data-processing and analyzing a plurality of diffraction profiles.