Skellam Distribution Screening for Solid State Ionic Conductors
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Solution Overview
Problem
Existing methods for screening solid state ionic conductors for electrochemical devices are inefficient due to long computational execution times and high costs, limiting the evaluation of candidate materials for practical applications.
Innovation Solution
A method using a Skellam distribution to estimate diffusivity from atomic displacement calculations, allowing for rapid identification of preferred ionic conductor materials by simulating inorganic crystal structures at short simulation times and comparing results to a threshold value, reducing computational time and resource costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple molecular dynamics calculations are performed to calculate diffusivity values until convergence is achieved, then measurement precision of diffusivity is improved, but productivity of material screening deteriorates due to long computational execution times
Solution Approach 1:
The patent applies partial action by performing molecular dynamics calculations for a fixed, limited simulation time rather than continuing until full convergence. This allows obtaining diffusivity estimates without completing the full convergence process, thereby reducing computational time while still providing useful screening results. The method accepts that not all calculations will achieve full convergence, but this is sufficient for the screening purpose.
Solution Approach 2:
The patent changes the parameter of simulation time from a convergence-based variable to a fixed parameter. By setting a predetermined simulation time duration, the method standardizes the computational effort across all material candidates, enabling high-throughput screening while maintaining consistent comparison criteria. This parameter change transforms the process from quality-oriented (convergence) to quantity-oriented (throughput).
2Productivity
If simulation time is reduced to shorten computational execution time, then productivity of material screening is improved, but measurement precision of diffusivity deteriorates due to insufficient sampling
Solution Approach 1:
The patent uses multiple independent molecular dynamics trajectories (copies) of the same system rather than a single long trajectory. By running several shorter simulations with different initial conditions and averaging the results, the method achieves better statistical sampling efficiency. This copying approach allows obtaining reliable diffusivity estimates from shorter simulation times compared to a single extended simulation.
Solution Approach 2:
The patent performs preliminary molecular dynamics calculations to generate equilibrium configurations and velocity distributions before the actual diffusivity measurement. This preliminary action ensures that the system is properly equilibrated and that the short simulation time is used efficiently for measurement purposes, maximizing the information obtained from limited computational resources.
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
Enables the rapid evaluation of thousands of candidate materials, significantly reducing computational and laboratory costs while accurately identifying highly conductive materials for further analysis and synthesis, suitable for high-throughput screening before experimental testing.
Implementation Method 1
Computational methods based on molecular dynamics calculations have been used to predict candidate materials to be synthesized and tested for desirable material properties
Implementation Method 2
calculating by the processor an estimate of diffusivity from a Skellam distribution of the displacement
Data Source
AI summary
Non-normal statistics applied to diffusivity calculations accelerate screening of ionic conductors for electrochemical devices such as electric storage batteries, fuel cells, and sensors. Displacements of atomic species within a crystalline structure for a candidate ionic conductor material are analyzed using a Skellam distribution optionally combined with Gaussian noise to calculate values for the standard deviation, upper error bound, and lower error bound for predicted values of diffusivity (D). When the predicted values of D have sufficient statistical precision, the diffusivity calculation is terminated and the calculated diffusivity is compared to a threshold value of diffusivity. When the threshold has been exceeded, the candidate ionic conductor may be listed as a preferred good conductor. When the calculated diffusivity fails to exceed the threshold, the material may be listed as a poor conductor and may be eliminated from further consideration.