implementation

AI-Driven Design Proposal with Claude 3.5 Haiku

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Linked challenge: AI-Driven Supercapacitor Design via Pore Network & Electrolyte Optimization

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Linked challenge

AI-Driven Supercapacitor Design via Pore Network & Electrolyte Optimization

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Original prompt text with formatting preserved for inspection.

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Integrate Claude 3.5 Haiku, orchestrated by DSPy, to generate novel supercapacitor designs. Your DSPy program should prompt Claude to suggest combinations of pore network geometry parameters and electrolyte compositions, aiming to balance fast charging and high energy density. The initial prompts should encourage exploration of diverse materials and structures, incorporating insights from the provided news article on tortuosity. Ensure your DSPy modules are well-defined for structured output.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

AI-Driven Supercapacitor Design via Pore Network & Electrolyte Optimization

This challenge focuses on leveraging AI to accelerate the design of next-generation supercapacitors. Participants will develop a computational framework that intelligently explores and optimizes pore network architectures within electrode materials and proposes compatible electrolyte compositions to achieve superior charging speeds and energy densities. The emphasis is on building a robust design pipeline capable of integrating material property prediction with transport simulations. The solution will use advanced physics-informed machine learning techniques to predict key performance indicators (KPIs) from material structure and composition, thereby reducing the need for exhaustive experimental trials. By simulating tortuosity and ionic transport within complex pore geometries, developers will identify optimal material designs for faster ion diffusion and higher surface area utilization, driving innovation in energy storage materials and manufacturing.

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