173
views
0
recommends
+1 Recommend
2 collections
    1
    shares
      scite_
       
      • Record: found
      • Abstract: found
      • Poster: found
      Is Open Access

      Analysis of error distribution in catalyst-adsorbate binding energies generated by GNN

      poster
      Bookmark

            Abstract

            Scalable methods of storing renewable energy to reduce climate change require high- performance catalysts that can be discovered using Artificial Intelligence (AI), prompting data scientists to train models. In the OC20 dataset (IS2RE), the Graphormer model, a type of Graph Neural Network (GNN), performed the best. We hope to find trends in the error distribution of catalyst-adsorbate binding energies to explain and make improvements.

            GNNs are based on Graph Theory, which makes them optimised for certain data structures. In the context of molecules, a graph can be used to represent the atomic and molecular structure of a compound through its edges and nodes. By using a GNN, it is possible to effectively capture the intricate relationships and dependencies between the atoms and bonds in a molecule. This is important for predicting and understanding the properties and behaviour of the molecule.

            With Python and Jupyter Notebook, we had many cutting-edge packages and libraries at our disposal; some included are matplotlib, pandas, and Atomic Simulation Environment (ASE). Through the data exploration, wrangling, and analysis, several insights were gleaned.

            Smaller unit cells tended to have larger errors. Across the period, there are local minima of errors in Group 10. Chlorine, perhaps because it is the only halogen, had an abnormally large error. Adsorbates containing Nitrogen had greater errors, as opposed to ones that are grouped as C1, C2 or O/H only.

            Content

            Author and article information

            Journal
            ScienceOpen Posters
            ScienceOpen
            10 May 2023
            Affiliations
            [1 ] Anglo-Chinese School (Independent), 121 Dover Rd, Singapore 139650;
            [2 ] Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #21-01, Connexis South Tower, Singapore 138632, Republic of Singapore;
            Author notes
            Author information
            https://orcid.org/0000-0002-3589-3625
            Article
            10.14293/P2199-8442.1.SOP-.PIBJC1.v1
            e255b562-99c1-4a28-bb05-20703aeb3770

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            History
            : 10 May 2023
            Categories

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Catalysis,Organic & Biomolecular chemistry,Neural & Evolutionary computing,Artificial intelligence
            catalyst,neural networks,binding energies,graph neural network,error,transformer,graphormer

            Comments

            Comment on this article