OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features

• URL: http://arxiv.org/abs/2007.08026v3
• Date: Tue, 18 Jan 2022 19:04:36 GMT
• Title: OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
• Authors: Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, and Thomas F. Miller III
• Abstract summary: textscOrbNet is shown to outperform existing methods in terms of learning efficiency and transferability. For applications to datasets of drug-like molecules, textscOrbNet predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.
• Score: 42.96944345045462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-network architecture. \textsc{OrbNet} is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison, \textsc{OrbNet} predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.

Related papers

• Orbital-free density functional theory with first-quantized quantum subroutines [0.0]
  We propose a quantum-classical hybrid scheme for performing orbital-free density functional theory (OFDFT) using probabilistic imaginary-time evolution (PITE) PITE is applied to the part of OFDFT that searches the ground state of the Hamiltonian in each self-consistent field (SCF) iteration. It is shown that obtaining the ground state energy of Hamiltonian requires a circuit depth of $O(log N_mathrmg)$.
  arXiv  Detail & Related papers  (2024-07-23T05:34:11Z)
• Machine learning Hubbard parameters with equivariant neural networks [0.0]
  We present a machine learning model based on equivariant neural networks. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations. Our model achieves mean absolute relative errors of 3% and 5% for Hubbard $U$ and $V$ parameters, respectively.
  arXiv  Detail & Related papers  (2024-06-04T16:21:24Z)
• Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials [3.7655047338409893]
  ChargE3Net is an E(3)-equivariant graph neural network for predicting electron density in atomic systems. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials.
  arXiv  Detail & Related papers  (2023-12-08T21:56:19Z)
• Electronic excited states from physically-constrained machine learning [0.0]
  We present an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on.
  arXiv  Detail & Related papers  (2023-11-01T20:49:59Z)
• 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)
• Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry [51.827942608832025]
  Near-term quantum computers can calculate the ground-state properties of small molecules. We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
  arXiv  Detail & Related papers  (2021-12-31T16:33:10Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• 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)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  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.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

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.