AI Directed Zeolite Synthesis via Deep Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current zeolite synthesis and design processes are inefficient due to the complexity of zeolite structure prediction from synthesis parameters, limited predictive methodologies, and the need for designing specific zeolite catalysts for chemical reactions, molecular adsorptions, and diffusions in the oil and gas industry.
Innovation Solution
The use of AI-directed zeolite synthesis through machine learning algorithms and models, specifically vectorized cognitive deep learning neural networks, to design and construct zeolite catalysts by extracting historical data, converting it into graph models, and training algorithms to predict catalyst chemical reaction models and production yields, thereby automating the construction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional hydrothermal synthesis methods are used for zeolite construction, then zeolite structures can be produced, but the synthesis process is inefficient and lacks predictive ability due to complex crystallization kinetics and numerous synthesis parameters
Solution Approach 1:
The patent transforms the complex set of synthesis parameters into a simplified predictive framework using machine learning models. By training on historical synthesis data, the system learns optimal parameter combinations and relationships, converting numerous independent parameters into interconnected predictive relationships that guide synthesis without requiring manual optimization of each parameter.
Solution Approach 2:
The patent replaces traditional trial-and-error experimental methods with computational prediction systems. Machine learning algorithms and neural networks substitute for manual synthesis planning, automatically analyzing synthesis parameters and predicting outcomes, thereby eliminating the need for extensive experimental iteration and improving synthesis efficiency.
2Loss of time
If computational design methods are used to predict zeolite structures, then design efficiency can be improved, but predictive ability is currently limited by lack of robust methodologies
Solution Approach 1:
The patent performs preliminary computational analysis and prediction before actual synthesis. By using machine learning models to predict synthesis outcomes and structure formation in advance, the system allows researchers to evaluate multiple design scenarios computationally before committing to physical synthesis, significantly reducing design time while improving reliability through data-driven predictions.
Solution Approach 2:
The patent implements feedback loops where synthesis results are fed back into the machine learning models to continuously improve predictive accuracy. Historical synthesis data and experimental outcomes are used to retrain and refine the algorithms, creating a self-improving system that becomes more reliable with each iteration while maintaining rapid design capabilities.
3Productivity
If AI and machine learning algorithms are implemented for zeolite synthesis direction, then predictive ability and synthesis efficiency are enhanced, but the complexity of the system increases
Solution Approach 1:
The patent develops universal machine learning models that can handle multiple synthesis scenarios and predict various zeolite structures using the same underlying algorithmic framework. Rather than creating specialized systems for each zeolite type or synthesis condition, the system uses generalizable AI models that adapt to different applications, managing complexity through shared computational infrastructure.
Solution Approach 2:
The patent creates simplified digital representations and models of complex synthesis systems. By developing computational proxies that simulate synthesis behavior without requiring full physical experimentation, the system manages complexity through virtual modeling, allowing researchers to work with simplified digital twins of the synthesis process rather than directly managing all physical parameters.
Data Source
AI summary
A computer implemented method for designing chemical reactions for catalyst construction is described. The method includes extracting historical data including historic chemical reaction data and historic catalyst construction yield data and converting the historic chemical reaction data into graph models to represent molecular structure data. The method also includes incorporating the graph models into a chemical reaction algorithm and training a vectorized cognitive deep learning network of the chemical reaction algorithm by using the graph models and a property of the historic chemical reaction data to produce a catalyst chemical reaction model. Further, the method includes validating the catalyst chemical reaction model by inputting the historic chemical reaction data and comparing a generated property corresponding to the catalyst chemical reaction model to the property of the historic chemical reaction data. Lastly, the method includes updating the training of the catalyst chemical reaction model.