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The modular architecture can be applied across domains, offering a generalizable blueprint for trustworthy AI-assisted knowledge synthesis to circumvent corrosion, which can also be applied to other engineering domains.
This work presents a novel approach toward high‐energy molecules by combining long short‐term memory (LSTM) networks for molecular generation and attentive graph neural networks (GNN) for property predictions by combining fixed SHA‐256 embeddings with partially trainable representations.
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