Composite Prediction GNN & Agentic LLM

@Tabout

Hello, I can build a reproducible end-to-end Composite Prediction stack: (1) Graph schema + preprocessing to convert your fibre/matrix/interface data into PyTorch Geometric HeteroData, with frozen feature specs and validation. (2) Hetero-GNN regressor (GINE/SAGE/GATv2) for multi-target mechanical/thermal properties, supporting missing labels, with metrics + error analysis, checkpoints + scalers. (3) Agentic LLM layer (LangChain/LangGraph) that interprets user queries, orchestrates feature engineering, calls the GNN tool, and explains results (and limitations/uncertainty). (4) FastAPI endpoints /predict and /chat, plus runnable scripts/notebooks and clear documentation for retraining. To start, please share 10–50 representative samples, target list + units/ranges, your intended node/edge semantics, and success criteria. I will deliver clean, maintainable code and a handover guide so you can extend and fine-tune later. Best regards

@sarazahran909

Hi, I’m Sara Zahran, an AI specialist with strong expertise in Graph Neural Networks (GNNs), Large Language Models (LLMs), and materials science applications. I can build a fully reproducible end-to-end pipeline that combines a GNN for predicting composite material properties with an autonomous LLM agent for query-driven interaction and analysis. Here’s how I will approach your project: - GNN Modeling: Implement a robust GNN (using PyTorch Geometric) to learn latent relationships in fibre, matrix, and interface graphs, delivering accurate predictions of mechanical and thermal properties. - LLM Agent Layer: Build a conversational agent (via LangChain or a lightweight custom wrapper) to orchestrate feature engineering, trigger model runs, and explain results in plain English. - Reproducible Workflow: Provide fully runnable notebooks/scripts, environment files, and a clean, maintainable codebase ready for extension or fine-tuning on new composite data. - Documentation & Clarity: Deliver concise, clear documentation so you can continue development or integrate into cloud deployment easily. I have hands-on experience with advanced GNNs and autonomous AI agents, and I can start immediately to deliver a high-quality, production-ready solution within your timeline. Looking forward to collaborating and discussing specifics!

@MuhammadAsad108

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Project Requirements & Raw Data ⟶⟶ Problem Understanding & Planning ⟶⟶ Model / System Design ⟶⟶ Implementation & Optimization ⟶⟶ Testing & Validation ⟶⟶ Final Delivery & Documentation Key Highlights ✔ End-to-end solution — complete implementation from concept to final delivery. ✔ Clean & structured workflow — well-organized pipeline with clear logic and reproducible results. ✔ Professional implementation — industry-standard practices, readable code, and proper documentation. ✔ Data handling & preprocessing — efficient feature engineering and preparation based on project needs. ✔ Model development — accurate, optimized, and well-explained results aligned with project objectives. ✔ Clear outputs — meaningful visualizations, reports, or demos (as required). ✔ Editable & reusable — fully modifiable project files for future improvements. ✔ On-time delivery — first working version shared quickly, followed by refinements based on feedback. Best Regards, Hamza AI / ML Engineer | Data Analysis | End-to-End Project Delivery

@Cassiusred

This is exactly the kind of system I build. GNN for materials property prediction combined with an LLM agent layer for conversational interaction. Relevant experience: - Built PyTorch Geometric models for structured data, including property prediction from graph representations - Production LLM agent systems using LangChain and custom orchestration for tool-calling, reasoning, and API integration - ML pipelines with proper versioning, reproducibility, and clear documentation My approach for your project: 1. GNN architecture: Message-passing network designed for composite graphs (fibre/matrix/interface nodes, edge relations). I would likely start with a GATv2 or SchNet-style approach depending on your data structure. 2. Agent layer: LangChain or a leaner custom wrapper that exposes the GNN as a tool. The agent handles query interpretation, triggers inference, and explains outputs conversationally. 3. Deliverables: Runnable notebooks, environment files, modular codebase, and documentation for extending or fine-tuning. I have questions about your dataset structure (are node features tabular? what are the target properties?) that would help me scope this more precisely. Happy to discuss.

@mohamedelamra

I can help you build an end-to-end AI pipeline that combines Graph Neural Networks (GNNs) with an agentic Large Language Model to predict composite material performance before lab validation. My approach: Design and train a GNN (PyTorch Geometric) to learn latent structural relationships from fibre, matrix, and interface graphs, predicting key mechanical or thermal properties. Build an LLM-based agent layer (LangChain or custom) that: Interprets user queries Orchestrates feature engineering and model execution Explains predictions clearly in natural language via an API. Deliver a reproducible workflow with clean code, environment files, and runnable notebooks/scripts. Provide clear documentation so the system can be extended or fine-tuned on new composite datasets. I focus on maintainable architectures, strong ML fundamentals, and explainable AI, making advanced models usable by non-ML experts. Happy to discuss timelines, model design choices, and optimisation strategies.

@AIbyNaveed

Hello, I can build an end-to-end pipeline combining a GNN (PyTorch Geometric) with an agentic LLM (LangChain or custom) to predict composite material performance. I’ll deliver a trained GNN, an autonomous LLM agent for reasoning and interaction, reproducible workflows, and clear documentation for easy extension. I’ve worked on predictive modeling and AI agent integration and can provide a clean, maintainable implementation.

@manishmahto378

Hi, This is exactly the kind of hybrid modelling problem I specialise in—combining **Graph Neural Networks for scientific prediction** with an **agentic LLM layer** for orchestration and explanation. How I’ll approach it * Design and train a **GNN (PyTorch Geometric)** to model fibre, matrix, and interface graphs and predict mechanical/thermal properties * Implement clean graph construction, feature encoding, and validation to ensure strong generalisation * Build an **LLM agent layer** (LangChain or a lightweight custom wrapper) that: * Interprets user queries * Triggers feature engineering and GNN inference * Reasons over outputs and explains results in plain English * Deliver a fully reproducible training and inference workflow with scripts and/or notebooks Deliverables * Well-structured, documented GNN codebase * Agentic LLM interface callable via API * Environment files for easy reproduction * Clear documentation for extension and future fine-tuning What you’ll get * An end-to-end, maintainable pipeline from material graphs to interpretable predictions * Modular design so you can evolve the GNN or LLM independently * A research-grade implementation ready for deployment once integrated with your cloud stack I have experience with **GNNs for scientific data** and **autonomous LLM agents**, and I’m happy to discuss prior work, timelines, and dataset specifics to ensure a strong technical fit. Ready to get started when you are.

@Jatin1Bagga

Hi there, I am a Data Scientist with experience in building end-to-end ML pipelines. I am very interested in your project coupling Graph Neural Networks (GNN) with Agentic LLMs for material science predictions. I have the exact technical stack you are looking for: GNN Layer: I will use PyTorch Geometric (PyG) to process your structural graphs. I can implement message-passing networks (like GCN or GAT) to capture the latent relationships between fiber, matrix, and interface data. Agentic Layer: I will use LangChain to build the autonomous agent. I will set up tools that allow the LLM to query the GNN model, interpret the numerical output (mechanical/thermal properties), and explain it in plain English. Workflow: I will provide clean, reproducible Jupyter notebooks and a modular codebase. I am comfortable working with raw material datasets and can ensure the code is documented for future fine-tuning. Let's discuss your specific dataset structure