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Chem World is introduced, a comprehensive benchmark for chemical property prediction that integrates 17 diverse chemical datasets with over 800,000 molecular samples, covering various properties including density, electrical conductivity, solubility, and other molecular characteristics and Mixture-PINN is proposed, a physics-informed neural network based prediction framework that incorporates chemical prior knowledge into data-driven learning.
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