Efficient, interpretable graph neural network representation for angle-dependent properties and its application to optical spectroscopy


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{
  "revision": 7, 
  "metadata": {
    "publication_date": "May 23, 2022, 09:17:24", 
    "_oai": {
      "id": "oai:materialscloud.org:1353"
    }, 
    "license": "Creative Commons Attribution 4.0 International", 
    "description": "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 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 representing transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.", 
    "contributors": [
      {
        "familyname": "Hsu", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "email": "hsu16@llnl.gov", 
        "givennames": "Tim"
      }, 
      {
        "familyname": "Pham", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "email": "pham16@llnl.gov", 
        "givennames": "Tuan Anh"
      }, 
      {
        "familyname": "Keilbart", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "givennames": "Nathan"
      }, 
      {
        "familyname": "Weitzner", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "givennames": "Stephen"
      }, 
      {
        "familyname": "Chapman", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "givennames": "James"
      }, 
      {
        "familyname": "Xiao", 
        "affiliations": [
          "Dalhousie University, Halifax, NS, Canada"
        ], 
        "givennames": "Penghao"
      }, 
      {
        "familyname": "Qiu", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "givennames": "S. Roger"
      }, 
      {
        "familyname": "Chen", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "givennames": "Xiao"
      }, 
      {
        "familyname": "Wood", 
        "affiliations": [
          "Lawrence Livermore National Laboratory, Livermore, CA, United States"
        ], 
        "email": "wood37@llnl.gov", 
        "givennames": "Brandon"
      }
    ], 
    "edited_by": 576, 
    "title": "Efficient, interpretable graph neural network representation for angle-dependent properties and its application to optical spectroscopy", 
    "conceptrecid": "1352", 
    "license_addendum": null, 
    "doi": "10.24435/materialscloud:s7-8e", 
    "mcid": "2022.66", 
    "_files": [
      {
        "size": 68820351, 
        "key": "for-reproducing-figures.tar.gz", 
        "checksum": "md5:cdb026366224747f6fee0b89c16cbc60", 
        "description": "Data for reproducing figures"
      }, 
      {
        "size": 92855262, 
        "key": "for-reproducing-model-training.tar.gz", 
        "checksum": "md5:31bbff1d8be5a2a7a32ff0d5649a9a3b", 
        "description": "Data for reproducing model training"
      }
    ], 
    "id": "1353", 
    "keywords": [
      "Graph neural network", 
      "Spectroscopy", 
      "Aqua complex"
    ], 
    "is_last": true, 
    "status": "published", 
    "references": [
      {
        "url": "https://arxiv.org/abs/2109.11576", 
        "type": "Preprint", 
        "citation": "T. Hsu, T. Pham, N. Keilbart, S. Weitzner, J. Chapman, P. Xiao, S. Qiu, X. Chen, B. Wood, arXiv preprint arXiv:2109.11576 (2021)."
      }
    ], 
    "version": 1, 
    "owner": 764
  }, 
  "id": "1353", 
  "created": "2022-05-20T02:41:29.754942+00:00", 
  "updated": "2022-05-23T07:17:24.366170+00:00"
}