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An accurate force field is the key to the success of all molecular mechanicssimulations on organic polymers and biomolecules. Accuracy beyond densityfunctional theory is often needed to describe the intermolecular interactions,while most correlated wavefunction (CW) methods are prohibitively expensive forlarge molecules. Therefore, it posts a great challenge to develop an extendibleab initio force field for large flexible organic molecules at CW level ofaccuracy. In this work, we face this challenge by combining the physics-drivennonbonding potential with a data-driven subgraph neural network bonding model(named sGNN). Tests on polyethylene glycol polymer chains show that ourstrategy is highly accurate and robust for molecules of different sizes.Therefore, we can develop the force field from small molecular fragments (withsizes easily accessible to CW methods) and safely transfer it to largepolymers, thus opening a new path to the next-generation organic force fields.

An Extendible, Graph-Neural-Network-Based Approach for Accurate Force Field Development of Large Flexible Organic Molecules