Neural Network Reaction Condition Optimization for Stable High Yields
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Solution Overview
Problem
Existing methods for synthesizing chemical compounds require numerous experiments and significant human resources, making it difficult to identify stable synthetic conditions efficiently.
Innovation Solution
A neural network-based method that uses a graph-type descriptor to optimize synthetic conditions by determining initial condition combinations, updating a prediction model with ground-truth yields, and iteratively refining priorities to achieve high yields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional experimental methods are used to synthesize chemical compounds, then comprehensive data can be obtained through numerous experiments, but the number of experiments required is large and human resources are significantly consumed
Solution Approach 1:
The patent applies preliminary action by training a neural network model in advance with historical experimental data to predict optimal synthetic conditions before actual experiments are conducted. The model pre-processes and learns from existing data to guide subsequent experimental design, reducing the need for extensive trial-and-error experiments.
Solution Approach 2:
The patent implements feedback through an iterative optimization process where experimental results are fed back into the neural network model to update and refine predictions. The system continuously improves by incorporating actual yield data and experimental outcomes to enhance the accuracy of condition recommendations for subsequent experiments.
2Reliability
If numerous experiments are conducted to identify stable synthetic conditions, then reliable data can be obtained, but the process requires significant human resources and time
Solution Approach 1:
The patent introduces an intermediary neural network model that acts as a mediator between historical data and experimental design. This intermediary system processes and interprets complex patterns in synthetic condition data, providing guided recommendations that improve the efficiency of identifying stable conditions without requiring exhaustive experimentation.
Solution Approach 2:
The patent applies parameter changes by using the neural network to predict and optimize multiple synthetic condition parameters simultaneously (temperature, pressure, catalyst amounts, reaction time, etc.). The model learns optimal parameter combinations from historical data and recommends adjusted parameters to achieve desired product yields, improving identification efficiency.
3Loss of time
If a neural network model is used to predict synthetic conditions, then the number of experiments can be reduced, but the model requires training data and iterative updates
Solution Approach 1:
The patent applies universality by designing a multi-functional neural network model that performs multiple tasks: predicting optimal synthetic conditions, estimating product yields, identifying suitable reaction types, and guiding experimental design. This single model handles diverse functions that would otherwise require separate systems, managing complexity through consolidation.
Solution Approach 2:
The patent implements dynamics through an adaptive neural network model that evolves and updates its parameters based on incoming experimental data. The model transitions from static initial predictions to dynamic, continuously improving recommendations as it learns from actual experiment outcomes, allowing it to adapt to new information and improve accuracy over time.
Data Source
AI summary
A method of optimizing synthetic conditions includes receiving a graph-type descriptor comprising at least one of structural information of at least one reactant and structural information of a target product to be synthesized by the reactant; determining combinations of synthetic conditions for generating the target product by applying the graph-type descriptor to a prediction neural network model; selecting at least one initial condition combination from among the combinations based on a first confidence corresponding to a yield of the combinations; updating the prediction neural network model based on a ground-truth yield obtained from a result of an experiment with the initial condition combination; determining a priority of the combinations based on the updated prediction neural network model; and determining subsequent combinations of synthetic conditions based on the determined priority.