Inference Device Dynamic Algorithm Switching for Geometry Optimization
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Solution Overview
Problem
Traditional geometry optimization processes for substances composed of multi-bodied atomic nuclei and electrons face challenges in convergence rate and robustness.
Innovation Solution
An inference device employing multiple algorithms, switching between a robust first algorithm like FIRE and a high-convergence second algorithm like LBFGS based on force thresholds, to iteratively compute and optimize the geometry of substances, ensuring stable and efficient optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single geometry optimization algorithm is used, then the process is simple, but the convergence rate and robustness are insufficient
Solution Approach 1:
The system dynamically switches between different geometry optimization algorithms (FIRE and LBFGS) based on the convergence state of the optimization process. The algorithm selection is not static but adapts during execution, allowing the system to leverage the robustness of FIRE in early stages and the high convergence rate of LBFGS when approaching the minimum energy structure.
Solution Approach 2:
The system changes the algorithm parameter (which algorithm is active) based on the optimization progress. By monitoring convergence criteria and switching between algorithms with different characteristics, the system optimizes both robustness and convergence rate without requiring complex manual intervention.
2Productivity
If a single geometry optimization algorithm is used, then the process is easy to operate, but the convergence rate is slow
Solution Approach 1:
The system employs dynamic algorithm switching where the optimization algorithm changes based on real-time convergence performance. When the optimization process shows slow convergence with one algorithm, the system switches to another algorithm better suited for the current optimization stage, thereby maintaining high productivity throughout the process.
Solution Approach 2:
The geometry optimization process is segmented into different phases, with different algorithms applied to different phases. The FIRE algorithm is used for initial optimization where robustness is needed, while LBFGS is applied when closer to convergence where speed is critical. This segmentation allows each algorithm to operate in its optimal performance regime.
3Reliability
If manual algorithm switching is required, then algorithm selection can be optimized, but user intervention is needed constantly
Solution Approach 1:
The system performs self-service by automatically monitoring its own optimization progress and autonomously switching between algorithms based on predefined convergence criteria. The system evaluates its own performance metrics and makes algorithm selection decisions without external user intervention, maintaining both optimization stability and ease of operation.
Solution Approach 2:
The system implements feedback mechanisms where convergence metrics from the optimization process are continuously monitored and fed back to the algorithm selection logic. This feedback loop enables automatic, data-driven algorithm switching that maintains optimization stability while eliminating the need for constant manual user intervention.
Data Source
AI summary
According to one embodiment, an inference device includes at least one memory and at least one processor. The at least one processor performs a computation for geometry optimization of a substance by a first algorithm. After a predetermined condition is satisfied, the at least one processor performs, based on a result of the computation by the first algorithm, a geometry optimization of the substance by a second algorithm different from the first algorithm.