Flows and transport phenomena in confined spaces have emerged as a key direction in modern fluid dynamics research[1].Scaling down the hydrodynamic length of a system does not simply lead to a laminar flow in low Reyn...Flows and transport phenomena in confined spaces have emerged as a key direction in modern fluid dynamics research[1].Scaling down the hydrodynamic length of a system does not simply lead to a laminar flow in low Reynolds number,but reveals plenty of new phenomena with novel technological implications.Unlike in macroscale systems,fluid behavior at micro-and nanoscales is governed by forces that act at or near the interfaces,including surface tension,wettability,van der Waals interactions,and electrostatic effects,etc.These interfacial forces produce new hydrodynamics and mass transport phenomena that have not been observed on large scales,which are widely used in multidisciplinary areas.展开更多
Graph Neural Network(GNN)potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs.Message-passing GNNs model interactions beyond their immediate neigh...Graph Neural Network(GNN)potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs.Message-passing GNNs model interactions beyond their immediate neighborhood by propagating local information between neighboring particles while remaining effectively local.However,locality precludes modeling long-range effects critical to many real-world systems,such as charge transfer,electrostatic interactions,and dispersion effects.In this work,we propose the Charge Equilibration Layer for Long-range Interactions(CELLI)to address the challenge of efficiently modeling non-local interactions.This novel architecture generalizes the classical charge equilibration(Qeq)method to a model-agnostic building block for modern equivariant GNN potentials.Therefore,CELLI extends the capability of GNNs to model longrange interactions while providing high interpretability through explicitly modeled charges.On benchmark systems,CELLI achieves state-of-the-art results for strictly local models.CELLI generalizes to diverse datasets and large structureswhile providing high computational efficiency and robust predictions.展开更多
文摘Flows and transport phenomena in confined spaces have emerged as a key direction in modern fluid dynamics research[1].Scaling down the hydrodynamic length of a system does not simply lead to a laminar flow in low Reynolds number,but reveals plenty of new phenomena with novel technological implications.Unlike in macroscale systems,fluid behavior at micro-and nanoscales is governed by forces that act at or near the interfaces,including surface tension,wettability,van der Waals interactions,and electrostatic effects,etc.These interfacial forces produce new hydrodynamics and mass transport phenomena that have not been observed on large scales,which are widely used in multidisciplinary areas.
基金Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive AgencyNeither the European Union nor the granting authority can be held responsible for them. This work was funded by the ERC (StG SupraModel) - 101077842the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 534045056 and 561190767.
文摘Graph Neural Network(GNN)potentials relying on chemical locality offer near-quantum mechanical accuracy at significantly reduced computational costs.Message-passing GNNs model interactions beyond their immediate neighborhood by propagating local information between neighboring particles while remaining effectively local.However,locality precludes modeling long-range effects critical to many real-world systems,such as charge transfer,electrostatic interactions,and dispersion effects.In this work,we propose the Charge Equilibration Layer for Long-range Interactions(CELLI)to address the challenge of efficiently modeling non-local interactions.This novel architecture generalizes the classical charge equilibration(Qeq)method to a model-agnostic building block for modern equivariant GNN potentials.Therefore,CELLI extends the capability of GNNs to model longrange interactions while providing high interpretability through explicitly modeled charges.On benchmark systems,CELLI achieves state-of-the-art results for strictly local models.CELLI generalizes to diverse datasets and large structureswhile providing high computational efficiency and robust predictions.
