Graph Neural Network for Hamiltonian-Based Material Property Prediction

• URL: http://arxiv.org/abs/2005.13352v1
• Date: Wed, 27 May 2020 13:32:10 GMT
• Title: Graph Neural Network for Hamiltonian-Based Material Property Prediction
• Authors: Hexin Bai, Peng Chu, Jeng-Yuan Tsai, Nathan Wilson, Xiaofeng Qian, Qimin Yan, Haibin Ling
• Abstract summary: We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
• Score: 56.94118357003096
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties. Traditional electronic structure methods require expensive computation time and memory consumption, thus a fast and accurate prediction model is desired with increasing importance. Representing the interactions among atomic orbitals in any material, a material Hamiltonian provides all the essential elements that control the structure-property correlations in inorganic compounds. Effective learning of material Hamiltonian by developing machine learning methodologies therefore offers a transformative approach to accelerate the discovery and design of quantum materials. With this motivation, we present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The information of each orbital includes the name, relative coordinates with respect to the center of super cell and the atom number, while the interaction between orbitals are represented by the Hamiltonian matrix. The results show that our model can get a promising prediction accuracy with cross-validation.

Related papers

• QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules [69.25826391912368]
  We generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 999 or 2998 molecular dynamics trajectories. We show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules.
  arXiv  Detail & Related papers  (2023-06-15T23:39:07Z)
• Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer [50.24983453990065]
  We show that the cQDO model lends itself naturally to simulation on a photonic quantum computer. We calculate the binding energy curve of diatomic systems by leveraging Xanadu's Strawberry Fields photonics library. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond.
  arXiv  Detail & Related papers  (2023-06-14T14:44:12Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain [0.0]
  We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials. This technique applies to important classes of materials such as nanotubes, nanoribbons, nanowires and nano-assemblies.
  arXiv  Detail & Related papers  (2022-02-02T09:32:03Z)
• Programmable Interactions and Emergent Geometry in an Atomic Array [0.0]
  Nonlocal interactions govern the flow of information and the formation of correlations in quantum systems. We report on the realization of programmable nonlocal interactions in an array of atomic ensembles within an optical cavity. Our work opens broader prospects for simulating frustrated magnets and topological phases, investigating quantum optimization algorithms, and engineering new entangled resource states for sensing and computation.
  arXiv  Detail & Related papers  (2021-06-08T03:00:49Z)
• Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction [54.93890176891602]
  We propose a diStance-aware Molecule graph Attention Network (S-MAN) tailored to drug-target binding affinity prediction. As a dedicated solution, we first propose a position encoding mechanism to integrate the topological structure and spatial position information into the constructed pocket-ligand graph. We also propose a novel edge-node hierarchical attentive aggregation structure which has edge-level aggregation and node-level aggregation.
  arXiv  Detail & Related papers  (2020-12-17T17:44:01Z)
• Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces [39.228041052681526]
  We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms. It is shown to be transferable across chemical space due to the use of domain-specific features.
  arXiv  Detail & Related papers  (2020-11-05T06:48:46Z)
• Orbital Graph Convolutional Neural Network for Material Property Prediction [0.0]
  We propose the Orbital Graph Convolutional Neural Network (OGCNN), a crystal graph convolutional neural network framework. OGCNN includes atomic orbital interaction features that learn material properties in a robust way. We examined the performance of this model on a broad range of crystalline material data to predict different properties.
  arXiv  Detail & Related papers  (2020-08-14T15:22:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.