GFFMERGE
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GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
arXiv:2606.03232v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new chemical systems requires expensive retraining of foundation models. Inspired by model merging in vision and language processing, we introduce GFFMERGE, the first principled framework for closed-form model merging in GNNs. We exploit the linear structure of...