Hierarchical Catalyst Screening for Activity and Stability
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Solution Overview
Problem
Current methods for designing and optimizing catalysts for chemical reactions are time-consuming and resource-intensive, particularly when using wet bench and in silico approaches, which struggle to efficiently identify catalysts with desired properties.
Innovation Solution
A computer-based hierarchical high-throughput screening method that evaluates candidate catalysts for activity, stability, and selectivity by analyzing their structural and compositional differences relative to a reference catalyst, focusing on rate-limiting steps and energy barrier changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wet bench and in silico methods are used to identify catalyst features, then catalyst performance can be optimized, but the process requires excessive time and resources making it impracticable
Solution Approach 1:
The patent segments the catalyst design process into hierarchical levels: (1) high-level screening using machine learning models to evaluate thousands of candidate catalysts, (2) mid-level detailed analysis using density functional theory for promising candidates, and (3) low-level experimental validation. This segmentation allows rapid filtering at each level, reducing the overall time and computational resources required while maintaining optimization reliability
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing catalyst features, properties, and performance data in databases before the actual screening process. Machine learning models are pre-trained on existing catalyst data, enabling rapid evaluation of candidate catalysts without requiring extensive real-time computational resources, thus reducing time and resource requirements
2Reliability
If comprehensive catalyst screening is performed to identify desired properties, then catalyst performance is improved, but the complexity of the process increases making it impracticable
Solution Approach 1:
The patent divides the comprehensive catalyst screening into modular computational components: machine learning models for initial filtering, density functional theory calculations for detailed electronic structure analysis, and kinetic modeling for performance prediction. Each module handles specific aspects of catalyst evaluation, reducing overall process complexity while maintaining comprehensive property identification
Solution Approach 2:
The patent introduces machine learning models as intermediaries between large-scale catalyst data and detailed computational analysis. These models quickly identify promising candidate catalysts from extensive databases, filtering out unpromising candidates before they undergo complex density functional theory calculations, thus managing screening complexity while identifying desired properties
Data Source
AI summary
Provided herein are screening methods to select catalysts having a desired set of target properties from a reference catalyst, and catalysts so obtained, as well as related catalysts material, composition, methods and systems.