Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is...Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.展开更多
基金The authors are partially supported by the Laboratory Directed Research and Development(LDRD)program(20-SI-004)at Lawrence Livermore National LaboratoryThis work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under contract No.DE-AC52-07NA27344.
文摘Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds.However,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered systems.In this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures.ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components.Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity.Future directions for further development of ALIGNN-d are discussed.
