Atom-Coordinate Image Fourier Analysis for Unknown Molecule Discovery
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Solution Overview
Problem
Existing methods for searching novel materials are limited to molecules already stored in databases, preventing the discovery of unknown molecules that satisfy performance conditions.
Innovation Solution
An information processing device and method that generates atom-coordinate images, performs Fourier transformation, principal component analysis, and uses learned models to derive index values and identify principal component vectors correlated with molecule performance, enabling the discovery of unknown molecules that satisfy performance conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If database referencing is used to search for molecules, then search speed is improved, but the ability to discover unknown molecules is worsened
Solution Approach 1:
The patent performs preliminary actions by generating atom-coordinate images and power spectrum data from molecular structure data before actual molecule search. This preprocessing creates a foundation that enables both fast database searching and the discovery of unknown molecules by establishing a standardized representation method that can be applied to both known and unknown molecular structures.
Solution Approach 2:
The patent changes the parameter representation from traditional molecular structure data to atom-coordinate images and power spectrum data through Fourier transformation. This parameter transformation enables the system to handle both database searching and unknown molecule discovery by converting molecular structures into a unified image-based representation that can be processed by machine learning models trained on principal component analysis.
2Measurement precision
If traditional molecule search methods are used, then search accuracy for known molecules is improved, but the ability to identify novel molecules is worsened
Solution Approach 1:
The patent replaces the mechanical database matching system with a machine learning-based image analysis system. By converting molecular structures into atom-coordinate images and applying principal component analysis with Fourier transformation, the system substitutes traditional structure-based searching with an image recognition approach that can identify both known and unknown molecules based on their structural patterns rather than exact database matches.
Solution Approach 2:
The patent introduces atom-coordinate images and power spectrum data as intermediary representations between molecular structure and molecule identification. These intermediaries serve as a bridge that allows the system to process molecular information in a format suitable for both accurate identification of known molecules and discovery of novel molecules through machine learning pattern recognition.
3Productivity
If database comparison is performed, then search efficiency is improved, but the scope of discoverable molecules is worsened
Solution Approach 1:
The patent creates a universal processing framework that handles both database searching and novel molecule discovery through the same atom-coordinate image generation and principal component analysis pipeline. This multi-functional approach allows the system to efficiently process known molecules from databases while simultaneously enabling the discovery of unknown molecules by applying the same image-based representation and machine learning methods to new molecular structures.
Solution Approach 2:
The patent transitions from traditional one-dimensional molecular structure data to two-dimensional atom-coordinate images through Fourier transformation. This dimensional change expands the search space from conventional structure-based matching to image-based pattern recognition, thereby expanding the scope of discoverable molecules while maintaining search efficiency through the standardized image processing pipeline.
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
This approach raises the possibility of discovering unknown molecules that meet performance criteria beyond database limitations, improving the accuracy and efficiency of material discovery.
Implementation Method 1
a spectrum production section that performs a Fourier transformation on the atom-coordinate image to produce power spectrum data
Data Source
AI summary
An information processing device, including: a memory, and a processor coupled to the memory, wherein the processor is configured to: generate an atom-coordinate image expressing atomic coordinates in a molecule; perform a Fourier transformation on the atom-coordinate image to produce power spectrum data; perform principal component analysis on the power spectrum data so as to derive, from the power spectrum data, principal component vectors expressing basis vectors of the power spectrum data and principal component scores expressing contained quantities of the principal component vectors; derive index values expressing degrees of correlation between the principal component scores and a performance of the molecule; identify any principal component vectors that correlate with the molecule performance based on the index values; and output principal component power spectrum data that is power spectrum data corresponding to the principal component vectors.