A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.
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Mol-Debate applies multi-agent debate in an iterative loop with perspective orchestration to achieve state-of-the-art text-guided molecular design, scoring 59.82% exact match on ChEBI-20 and 50.52% weighted success on S2-Bench.
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A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.
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Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design
Mol-Debate applies multi-agent debate in an iterative loop with perspective orchestration to achieve state-of-the-art text-guided molecular design, scoring 59.82% exact match on ChEBI-20 and 50.52% weighted success on S2-Bench.
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