Colorant Material Search Using VAE Latent Space Optimization
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Solution Overview
Problem
Existing technologies do not effectively address the search for new colorant materials, which is distinct from the search for medicines as described in existing machine learning technologies.
Innovation Solution
A colorant material search method utilizing a VAE encoder and decoder to identify desired colorant materials based on physical properties, combined with a physical property prediction model for optimization processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If existing machine learning technologies for medicine search are applied to colorant materials, then the search process can be automated, but the search accuracy and effectiveness for colorant materials deteriorates due to lack of domain-specific optimization
Solution Approach 1:
The patent transforms discrete molecular structures into continuous latent space representations, enabling gradient-based optimization. This parameter transformation allows the use of continuous optimization algorithms (like CMA-ES) that can efficiently search for molecules satisfying multiple physical property constraints, thereby improving search accuracy while maintaining automation.
Solution Approach 2:
The patent introduces a variational autoencoder (VAE) as an intermediary that maps between discrete molecular structures and continuous latent space. This intermediary enables the translation of discrete chemical structure search problems into continuous optimization problems, allowing automated search with high accuracy for colorant materials.
2Reliability
If multiple physical properties are considered simultaneously in the search, then the quality of identified colorant materials improves, but the complexity of the search process increases
Solution Approach 1:
The patent separates the search process into two independent stages: (1) training phase where the VAE and physical property prediction model are trained on existing data, and (2) search phase where optimization algorithms search the latent space. This segmentation allows complex multi-property optimization to be handled systematically, improving identification quality while managing process complexity through structured workflow division.
Solution Approach 2:
The patent performs preliminary training of the VAE encoder-decoder and physical property prediction model before the actual material search. This preliminary action prepares the optimization landscape in advance, allowing the subsequent search process to efficiently handle multiple physical properties without retraining, thus improving reliability while controlling complexity.
3Ease of manufacture
If traditional trial-and-error methods are used for colorant material discovery, then the process is simple to implement, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces traditional mechanical trial-and-error experimentation with a computational optimization system. The VAE-based continuous representation combined with gradient-free optimization algorithms (like CMA-ES) substitutes wet-lab iteration with in-silico optimization, dramatically reducing discovery time while maintaining implementation simplicity through automated computational pipelines.
Solution Approach 2:
The patent creates virtual copies of molecular structures in the form of latent space vectors, allowing optimization algorithms to search and manipulate molecular representations computationally before synthesizing actual materials. This copying approach eliminates the need for physical trial-and-error, reducing time and resource consumption while keeping the process simple to implement.
Data Source
AI summary
To improve technologies for searching for colorant materials.A colorant material search method performed by an information processing apparatus 10 includes: training a VAE encoder 3 and a VAE decoder 4, the VAE encoder 3 receiving, as an input, colorant material information expressed in a predetermined notation and outputting latent variables corresponding to the colorant material information on a latent space 5, the VAE decoder 4 receiving, as inputs, any latent variables on the latent space 5 and outputting colorant material information expressed in the predetermined notation; and identifying, based on the VAE encoder 3, the VAE decoder 4, and data regarding a plurality of physical properties of the colorant material, a desired colorant material that satisfies all of the plurality of physical properties.