3D Material Structure Prediction From Small-Angle Scattering Data
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Solution Overview
Problem
Existing techniques fail to accurately predict the three-dimensional structure of materials while considering various structural factors, particularly lacking in predicting three-dimensional structures based on Talbot orientation information.
Innovation Solution
An information processing device and method that utilizes small angle scattering data and supplemental information to predict three-dimensional structures by employing a prediction model learned through a combination of three-dimensional and two-dimensional models, utilizing form and structure factors, and comparing outputs to narrow candidates and ranges of structure parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only small angle scattering data is used for prediction, then the prediction process is simple, but the prediction accuracy of three-dimensional structure is insufficient
Solution Approach 1:
The patent combines small angle scattering data with supplemental two-dimensional information (such as microscope images or spectral data) as dual inputs to the prediction model. This merging of multiple data sources enables accurate three-dimensional structure prediction while maintaining model feasibility through integrated processing.
Solution Approach 2:
The patent integrates two-dimensional supplemental information with three-dimensional scattering data, creating a multi-dimensional input framework. This dimensional integration allows the model to leverage complementary information from different measurement modalities to achieve accurate 3D structure prediction.
2Measurement precision
If various structural factors are considered in prediction, then the structural analysis precision is improved, but the computational complexity increases
Solution Approach 1:
The prediction model is segmented into distinct functional components: a small angle scattering data processing unit, a supplemental information processing unit, and an integration unit. This segmentation allows each component to handle specific structural factors independently, improving precision while managing computational complexity through modular architecture.
Solution Approach 2:
The patent transforms multiple structural factors (form factor, structure factor, particle size distribution, shape distribution) into standardized parameters that can be processed efficiently by the prediction model. This parameter transformation enables comprehensive structural analysis without proportionally increasing computational complexity.
3Measurement precision
If three-dimensional structure prediction is performed without supplemental information, then the processing is faster, but the prediction accuracy is insufficient
Solution Approach 1:
The supplemental two-dimensional information is processed in advance to extract relevant structural features and constraints before being combined with scattering data. This preliminary processing of supplemental information enables faster integration and reduces the overall prediction time while maintaining high accuracy.
Solution Approach 2:
The patent introduces an information integration mechanism that acts as an intermediary between scattering data and supplemental information. This intermediary efficiently combines the two data sources, extracting complementary structural insights without creating computational bottlenecks, thus maintaining fast processing speeds.
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
Accurately predicts three-dimensional structure parameters of materials, including form and structure factors, enhancing the precision of structural analysis.
Implementation Method 1
small angle scattering data obtained by measuring a material
Data Source
AI summary
An information processing device includes: an acquiring section acquiring small angle scattering data obtained by measuring a material, and supplemental information that is two-dimensional data of the material; and a predicting section that, by using the small angle scattering data and the supplemental information as input, predicts a three-dimensional structure of the material from output of a prediction model that is learned in advance and that is for predicting the three-dimensional structure of the material.