Parameter Generation Device Using Annealing Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the research field of new material exploration, it is challenging to efficiently generate parameter sets that satisfy complex conditions, as existing methods rely heavily on skilled intuition or random parameter setting, leading to inefficiencies and low success rates, especially when conditions are interrelated.
Innovation Solution
A parameter generation device and method that converts input conditions into a Hamiltonian model, generates an Ising model, and uses an annealing machine to derive parameter sets that satisfy desired conditions, allowing for efficient parameter generation and simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parameters are set randomly to satisfy conditions, then any parameter set can be generated, but the probability of generating parameter sets that satisfy complex conditions becomes very low
Solution Approach 1:
The patent replaces random parameter setting (mechanical/systematic approach) with an annealing machine-based optimization system. The annealing machine uses quantum or thermal annealing processes to efficiently search the parameter space and find parameter sets that satisfy complex conditions, substituting the inefficient random generation method with a physics-based optimization approach that guarantees finding satisfying parameter sets even when conditions are highly complex and interrelated.
2Reliability
If parameters are set based on skilled person's experience and intuition, then parameter sets satisfying conditions can be found, but the method has strong geriatric nature and is difficult to apply without a specific skilled person
Solution Approach 1:
The patent implements a self-service system where the annealing machine automatically optimizes parameter sets based on input conditions without requiring skilled person intervention. The system takes conditions as input, processes them through the annealing optimization algorithm, and outputs parameter sets that satisfy the conditions, making the process independent of individual expertise and readily applicable to any user with the necessary input data.
Solution Approach 2:
The patent substitutes the human expert's intuitive and experience-based parameter setting process with an automated annealing machine system. This replacement eliminates the dependency on skilled persons while maintaining or improving the reliability of finding satisfying parameter sets, as the annealing algorithm systematically explores the parameter space based on mathematical optimization principles rather than human intuition.
3Measurement precision
If all parameter combinations are tried systematically, then the optimal parameter set can be found, but the number of combinations is on an astronomical scale making it impossible to try all patterns
Solution Approach 1:
The patent utilizes phase transition principles through the annealing process, where the system transitions from a high-energy random state to a low-energy optimal state by gradually lowering the temperature parameter. This phase transition approach allows the system to efficiently navigate the parameter space and converge to optimal or near-optimal parameter sets without exhaustively searching all possible combinations, dramatically reducing the time required while maintaining solution quality.
Solution Approach 2:
The patent replaces systematic exhaustive search with physics-based annealing optimization. Instead of methodically trying all parameter combinations in a brute-force manner, the annealing machine uses thermal or quantum annealing processes to probabilistically explore the parameter space and efficiently converge to optimal solutions, substituting the time-consuming mechanical search with a faster physics-inspired optimization process.
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 enables the efficient generation of parameter sets that satisfy desired conditions, even in complex scenarios, by leveraging annealing machines to find optimal parameter combinations, thus improving the efficiency of material exploration processes.
Implementation Method 1
an annealing machine which performs annealing based on an Ising model
Data Source
AI summary
The input means 81 accepts input of a condition to be satisfied by a parameter. The model generation means 82 converts the input condition into a model represented by a Hamiltonian. The annealing process means 83 generates an Ising model from the converted model and inputs the generated Ising model to an annealing machine to perform annealing. The output means 84 converts an annealing result into the parameter and outputs the parameter.