Traditional chemical R&D is bottlenecked by the "Trial-and-Error" trap, where 90% of laboratory resources are spent on non-viable candidates. **hsiang-chemcat** utilizes a high-throughput AI navigation engine that acts as a physical filter—predicting reaction stability and feasibility *before* expensive laboratory resources are deployed.
We filter out non-reactive pathways in the digital twin environment, ensuring that only the most promising candidates enter the actual experimental phase.
By defining the desired transition state stability, our engine reverse-engineers the necessary ligand structures, drastically reducing random screening trials.
We map the "Phase Margin" of chemical systems to identify operational windows where reactions are most stable, preventing costly thermal runaway during scale-up.
Hu Chia Hsiang is a Master's researcher at National Taipei University of Technology (Taipei Tech), focusing on the convergence of Computational Chemistry and Control Theory. He has developed advanced analysis pipelines that map high-dimensional quantum mechanical data into dynamic stability indicators.
Proven Impact: His proprietary computational engine has demonstrated the ability to handle over 1.8 million deep physical features. In benchmark simulations, this framework achieved a **85% reduction in potential resource waste** by accurately predicting and filtering failed reaction candidates during the pre-experimental stage.
We are seeking collaborations with specialized chemical labs and industrial partners for pilot validation.
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