Li-ion Battery Material Screening via Quantum Simulation Segmentation
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Solution Overview
Problem
Current methods for developing lithium-ion batteries are time-consuming and costly due to the need for empirical experimental efforts to find new materials, and quantum simulations, while accurate, are CPU-intensive and not scalable for large systems, limiting the exploration of compositional variations in cathode and anode materials.
Innovation Solution
A method and computer program that use quantum mechanical calculations to predict lithium battery properties by selecting candidate structures, calculating delithiated structures' energies, and developing a functional form for voltage based on lithium concentration, allowing for faster and more efficient screening of materials for safety, cycling ability, and capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum simulations are used to predict battery properties, then measurement precision is improved, but productivity deteriorates due to CPU-intensive calculations
Solution Approach 1:
The patent segments the battery material system into smaller computational units (unit cells with specific lithium concentrations) that can be simulated independently using quantum mechanics. By dividing the complex battery system into manageable segments, accurate quantum simulations can be performed on each segment without requiring excessive computational resources for the entire system at once.
Solution Approach 2:
The patent performs preliminary quantum simulations to generate a database of energy values at different lithium concentrations before actual battery operation prediction. This preliminary action creates reference data that can be used for faster predictions without repeating full quantum simulations, thus improving productivity while maintaining measurement precision through the use of pre-computed accurate data.
2Measurement precision
If quantum simulations are used for material screening, then measurement precision is improved, but loss of time increases due to computational intensity
Solution Approach 1:
The patent divides the material screening process into segmented quantum simulations at discrete lithium concentration points, allowing accurate prediction of energy states without simulating every possible configuration in real-time, thus reducing total simulation time while maintaining precision.
Solution Approach 2:
The patent performs preliminary quantum simulations to build a database of energy values at various lithium concentrations before actual battery operation prediction. This preliminary action creates reference data that can be used for faster predictions without repeating full quantum simulations, thus reducing time loss while maintaining measurement precision through pre-computed accurate data.
Solution Approach 3:
The patent uses quantum simulation results to create functional forms (mathematical models) that copy and represent the complex quantum behavior in a simplified manner. These functional forms can then be used for rapid predictions without repeating the full quantum simulations, significantly reducing time loss while preserving the precision of the original quantum mechanical calculations.
3Ease of manufacture
If empirical experimental efforts are used to find new materials, then ease of manufacture is improved, but productivity deteriorates due to time-consuming trial and error
Solution Approach 1:
The patent replaces the mechanical trial-and-error experimental process with quantum mechanical simulations and functional form predictions. This substitution allows virtual screening of materials properties before physical synthesis, maintaining the systematic approach of empirical research while dramatically increasing productivity by eliminating repeated experimental cycles.
Solution Approach 2:
The patent performs preliminary quantum simulations and functional form development to predict material properties before actual synthesis and testing. This preliminary computational action guides the experimental process, reducing the need for random trial and error and accelerating materials development while maintaining ease of manufacture through targeted experimentation.
Data Source
AI summary
Methods, systems, and computer programs for selecting electrode materials for a lithium battery are presented. In one embodiment, a method includes an operation for developing models for structural and energy analysis of battery stability, safety, cycling and performance, where the models are developed based on a selection of elements and compositions for the electrode materials. Properties of at least on cell performance parameter are estimated, and a cell discharge rate behavior is calculated. Another operation in the method is provided for selecting an electrode material composition based on the estimated properties and the cell discharge rate behavior. The method operations are performed by a computer system that includes a processor.