Inverse Catalyst Optimization with Transferable Reaction Descriptors
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Solution Overview
Problem
Existing catalyst design approaches are inefficient and computationally expensive, as they focus on direct composition and structure changes without considering transferable descriptors and thermodynamic constraints, leading to limited exploration of chemical space and difficulty in identifying optimal catalysts for complex reactions.
Innovation Solution
An inverse optimization method incorporating thermodynamic and scaling relation constraints, along with descriptor identification, to simplify catalyst design by identifying generalized descriptor values and screening new materials efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct catalyst design approaches are used to enhance catalytic performance through composition and structure changes, then catalytic performance can be improved, but the exploration of chemical space is limited and the process is inefficient
Solution Approach 1:
The patent inverts the traditional catalyst design approach by using inverse optimization: instead of directly searching for catalyst compositions that achieve desired performance, it formulates the problem as optimizing reaction energies to match target values, then works backwards to identify catalysts that satisfy these energy constraints. This inversion enables systematic exploration of chemical space while maintaining efficiency.
Solution Approach 2:
The patent transforms the catalyst design problem from direct composition/structure modification to parameter optimization of reaction energies. By changing the optimization variables from physical catalyst properties to reaction energy parameters, the method enables broader exploration of chemical space through mathematical optimization while maintaining connection to actual catalyst performance.
2Productivity
If inverse optimization technique is used to circumvent computational bottleneck, then screening efficiency is improved, but thermodynamic and equilibrium constraints cannot be satisfied and finding real catalysts with exact energies is difficult
Solution Approach 1:
The patent creates a universal optimization framework that simultaneously handles multiple constraints (thermodynamic, equilibrium, and energy targets) within a single mathematical formulation. This multi-functional approach allows the method to maintain high screening efficiency while reliably satisfying all necessary physical constraints through integrated constraint handling.
Solution Approach 2:
The patent incorporates feedback mechanisms where the optimization process continuously checks and adjusts reaction energy parameters against thermodynamic and equilibrium constraints. This feedback loop ensures that while maintaining screening efficiency, the method reliably identifies catalysts that satisfy all physical constraints by iteratively refining solutions.
3Speed
If descriptor-based screening is used for quick material screening, then preliminary screening speed is improved, but descriptors may not be transferable to different catalyst surfaces in complex reaction systems
Solution Approach 1:
The patent performs preliminary action by conducting sensitivity analysis and reaction pathway analysis before final catalyst screening. This preliminary characterization of reaction energies and their sensitivities creates a robust foundation that ensures descriptor transferability across different catalyst surfaces while maintaining quick screening capability through pre-established relationships.
Data Source
AI summary
The present invention is a method and device for simplification of interrelated complexities in a reaction relates to inverse optimization which relates to an algorithm which can potentially determine generalized descriptor values for various reaction systems, spanning pharmaceutical, polymeric, and other industries such as automobiles, specialty chemicals, and cosmetics. The invention lies in the hypothetical catalyst optimization step, and the core of the invention involves descriptor identification through hypothetical optimization in intricate systems like reaction networks. The aim of the optimization is to identify transferable descriptor and their value ranges to high performing catalyst solutions (i.e., activity, selectivity and stability). These value ranges will later be used for screening real materials with required catalytic performance.