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A Data-driven Quantification of Ligand Field Strength for the Design of Inorganic Complexe


  • Our latest work is accepted by JCCS (invited)!

  • Our latest work is available on ChemRxiv!

  • TYang lab Christmas/new year dinner! 


Our research bridges quantum chemistry (QC), machine learning (ML) and artificial intelligence (AI), and chemical understanding to solve the world’s most pressing problems: Energy supply and climate change. Our approaches build on the growing computing power to achieve Virtual High Throughput Screening (VHTS), which is complemented to the ample literature precedents to arrive at Property-constrained Deep Generative Models (pDGMs) for molecular design or is directly trained for Machine Learning (ML) Property Prediction. We further combine QC and ML to derive chemical understanding to foster comprehensions of chemical reactions and molecular design.


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  • Master's students | Mar 06, 2023

  • Ph.D. students | Mar 06, 2023

  • Postdoctoral researchers | Mar 06, 2023

  • Undergraduate students | Mar 06, 2023

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