The next generation of AI-driven catalyst design for solid-state hydrogen storage ($MgH_2$). Accelerating research from XRD characterization to predictive modeling.
XPEAK Ensemble Model
Feature Extraction...
Magnesium hydride (MgH2) is the future of solid-state hydrogen storage, but high dehydrogenation temperatures remain a significant barrier.
Cat MH bridges the gap between experimental characterization and theoretical prediction. By integrating Machine Learning, Large Language Models, and High-Throughput Screening, we accelerate the discovery of high-performance catalysts, drastically reducing the reliance on trial-and-error.
An XRD-Driven Machine Learning Platform for precise structural analysis and prediction.
Utilizes standardized XRD patterns and dimensionality reduction (NMF & kPCA) to capture intrinsic structural features.
Accurately predicts Dehydrogenation Peak Temperature ($T_p$) using ensemble models (XGBoost, LightGBM).
Rapidly evaluates 5,000+ candidate materials from ICDD to identify promising catalysts like KNbO3.
Provides interpretable insights into how catalyst loading and heating rates primarily affect performance.
Your Intelligent Multi-Agent Research Partner. Ask questions, get recommendations, and design autonomously.
Powered by Function Calling, integrating ML Prediction Agent and RAG Knowledge Base Agent.
Explores chemical space to autonomously "evolve" and discover optimal catalyst formulas.
Ask complex questions about mechanisms and get answers synthesized from thousands of papers.
Online | RAG & GA Active
Running Inverse Design (GA) & XPEAK Prediction...
Top Recommendation: Nb2O5 (doped)