Computational MOF Generation and Screening for Methane Storage
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Solution Overview
Problem
The challenge lies in efficiently identifying and synthesizing metal-organic frameworks (MOFs) with desired material properties for specific applications, such as improved methane storage, due to the vast number of possible combinations of building blocks, making it difficult to find MOFs with better or optimal properties.
Innovation Solution
A system and method for computationally generating and screening potential MOFs by combining inorganic and organic building blocks based on topological and geometrical information, using a generation module to predict material properties like surface area, pore size distribution, and methane storage capacity, and an evaluation module to perform atomistic grand Monte Carlo simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational methods are used to generate and screen potential MOFs from large libraries of building blocks, then the ability to identify MOFs with desired material properties is improved, but the complexity of the system and computational resources required increase
Solution Approach 1:
The system divides the complex task of MOF discovery into modular components: a generation module that creates hypothetical MOFs from building blocks, and a screening module that evaluates material properties. This segmentation allows each module to be optimized independently and manages the overall system complexity.
Solution Approach 2:
The patent introduces computational models and simulations as intermediaries between the physical building blocks and the desired material properties. These computational tools predict properties like surface area, porosity, and stability without requiring physical synthesis, reducing experimental complexity while maintaining prediction accuracy.
2Adaptability or versatility
If all possible combinations of building blocks are explored to find optimal MOFs, then the likelihood of finding MOFs with desired properties is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary computational screening of building block combinations before physical synthesis. By pre-evaluating material properties through simulations, the system identifies promising candidates in advance, avoiding exhaustive exploration of all possible combinations and significantly reducing the time required to find optimal MOFs.
Solution Approach 2:
The patent varies key parameters such as building block selection, topology, and functional groups to explore different regions of the MOF property space. By systematically changing these parameters and prioritizing combinations with predicted desirable properties, the system achieves broad adaptability without requiring complete enumeration of all possibilities.
3Productivity
If computational simulations are used to predict material properties, then the number of physical experiments required is reduced, but the accuracy of property prediction may be compromised
Solution Approach 1:
The system creates computational copies or models of physical MOF structures and uses these digital replicas to predict material properties. These virtual models allow rapid evaluation of numerous candidates without physical experiments, dramatically increasing discovery rate while maintaining acceptable prediction accuracy through validated computational methods.
Solution Approach 2:
The patent implements feedback loops where computational predictions are validated against experimental data when available, and results from physical synthesis of selected candidates are fed back to refine the computational models. This iterative feedback process continuously improves prediction accuracy while maintaining high productivity through computational screening.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach systematically identifies MOFs with enhanced methane storage capacity, such as NOTT-107, which demonstrates better performance than existing materials, and validates the predictive accuracy of the method through experimental confirmation.
Implementation Method 1
MOFs can be synthesized based on designs conceived a priori. This latter benefit stems from the use of modular molecular 'building blocks' that self-assemble into predictable crystal structures.
Implementation Method 2
an evaluation module to perform atomistic grand Monte Carlo simulations
Implementation Method 3
the pore size distribution, surface area, and methane storage capacity is calculated for the hypothetical MOFs
Data Source
AI summary
A system and method for systematically generating potential metal-organic framework (MOFs) structures given an input library of building blocks is provided herein. One or more material properties of the potential MOFs are evaluated using computational simulations. A range of material properties (surface area, pore volume, pore size distribution, powder x-ray diffraction pattern, methane adsorption capability, and the like) can be estimated, and in doing so, illuminate unidentified structure-property relationships that may only have been recognized by taking a global view of MOF structures. In addition to identifying structure-property relationships, this systematic approach to identify the MOFs of interest is used to identify one or more MOFs that may be useful for high pressure methane storage.