RESEARCH.md

MaterialForge: Operational Research & Hackathon Strategy

Event: Meta PyTorch OpenEnv Hackathon × Scaler School of Technology (Grand Finale) Dates: April 25–26, 2026 | Bangalore, India Theme Focus: World Modeling (#3.1) & Long-Horizon Planning (#2)

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1. Hackathon Context: The Grand Finale

This document serves as the internal reference for the MaterialForge project as it enters the in-person finale at Scaler School of Technology. The project is designed to showcase the power of OpenEnv in simulating complex, professional workflows—specifically Inverse Molecular Design.

Core Problem Statement

How can an AI agent autonomously discover stable, high-performance crystalline structures given a set of target physical properties and a finite resource budget?

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2. Strategic Theme Alignment

Theme #3.1: World Modeling (Professional Tasks)

MaterialForge is a high-fidelity simulation of a scientific workflow.

• Nuanced Interaction: The agent does not just "fill a grid"; it interacts with a physics engine (environment/physics.py) that models percolation pathways, coordination chemistry, and phase transitions.
• Causal Reasoning: The reward signal encourages agents to understand that stability (coordination number) and order (crystallinity) are prerequisites for achieving high-tier material properties.

Theme #2: (Super) Long-Horizon Planning

• Multi-step Reasoning: Designing a "Diamond-like" or "Superconductor Analogue" requires 50-80 coordinated steps.
• Sparse Reward Recovery: While the reward is dense, the ultimate goal (achieving a 'Crystalline' phase) is a delayed milestone that requires consistent, structured planning.
• Budget Management: Agents must track state over extended trajectories to ensure they don't exhaust the cost_budget before reaching the target symmetry.

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3. Project Analysis & Architecture

Repository Structure

Directory	Role	Key Files
environment/	Physics Engine	physics.py (heuristics), lattice.py (state), rubrics.py (rewards)
server/	Infrastructure	app.py (FastAPI), material_forge_env_environment.py (OpenEnv wrapper)
scenarios/	Task Logic	scenarios.py (Target property generation)
scripts/	Automation	validate-submission.sh (Compliance check)
tests/	Validation	benchmark.py (Baseline tracking)

Core Logic Breakdown

1. Lattice Engine: An 8x8 grid where actions (place, replace, remove) mutate the atomic ensemble.
2. Physics Heuristics: Calculates 4 properties (Hardness, Conductivity, Thermal Resistance, Elasticity) based on atom fractions, clustering bonuses, and BFS-based percolation checks.
3. Phase Classification: Categorizes the structure as Amorphous, Polycrystalline, or Crystalline based on 2x2 repeating pattern density and symmetry.
4. Reward Model: A composite score that weights property accuracy (50%) against structural integrity (stability + order + phase) (50%), minus efficiency penalties.

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4. Execution Flow

1. Initialization: The environment server starts via uvicorn.
2. Reset: The agent calls /reset, generating a target property vector (e.g., Hardness: 90, Conductivity: 10).
3. Inference Loop:
   • Agent observes the grid and current_properties.
   • Agent simulates potential moves using a local copy of the physics engine (Lookahead).
   • Agent selects an action to minimize the "property gap" while maximizing "stability".
4. Step: Action is sent to the server; the server returns the updated state and a dense reward.
5. Termination: Episode ends when targets are met within tolerance and the phase is Crystalline, or max_steps is reached.

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5. Submission Requirements & Judging Criteria

Minimum Requirements Checklist

• OpenEnv Usage: Fully compliant with Environment and EnvClient base classes.
• Training Script: (Planned) Implementation of a GRPO training loop using HF TRL or Unsloth.
• Evidence of Training: (Planned) Reward curves and before/after behavior comparison.
• Hosting: Environment is ready for deployment to Hugging Face Spaces.
• Mini-Blog/Video: (To be created) 2-minute walkthrough of the "Discovery Lab".

Judging Matrix (Targeting High Scores)

1. Environment Innovation (40%): MaterialForge introduces a novel scientific domain to RL, moving beyond simple games into professional discovery workflows.
2. Storytelling (30%): The "Discovery Lab" dashboard provides an engaging narrative of an AI "scientist" in a lab.
3. Showing Improvement (20%): We will demonstrate that a trained agent discovers "Crystalline" phases significantly faster than the heuristic baseline.
4. Pipeline Coherence (10%): Our reward logic is grounded in physical principles (Gibbs stability, Bragg order).

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6. Development Roadmap (Bangalore Finale)

1. Phase 1: Self-Improvement Strategy. Implement a training pipeline using Unsloth to fine-tune a model on successful discovery trajectories.
2. Phase 2: Multi-Agent Extension. (Optional) Introduce a "Critic" agent that suggests structural modifications to the "Worker" agent.
3. Phase 3: Final Validation. Run the validate-submission.sh script to ensure 100% compliance before the 3-minute pitch.

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Document updated for Finale Preparation: April 22, 2026