ELECTRA: A Symmetry-breaking Cartesian Network for Charge Density Prediction with Floating Orbitals

• URL: http://arxiv.org/abs/2503.08305v1
• Date: Tue, 11 Mar 2025 11:14:25 GMT
• Title: ELECTRA: A Symmetry-breaking Cartesian Network for Charge Density Prediction with Floating Orbitals
• Authors: Jonas Elsborg, Luca Thiede, Alán Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik,
• Abstract summary: We present an equivariant model for predicting electronic charge densities using "floating" orbitals.<n>Our method achieves a state-of-the-art balance between computational efficiency and predictive accuracy on established benchmarks.
• Score: 0.9736758288065405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using "floating" orbitals. Floating orbitals are a long-standing idea in the quantum chemistry community that promises more compact and accurate representations by placing orbitals freely in space, as opposed to centering all orbitals at the position of atoms. Finding ideal placements of these orbitals requires extensive domain knowledge though, which thus far has prevented widespread adoption. We solve this in a data-driven manner by training a Cartesian tensor network to predict orbital positions along with orbital coefficients. This is made possible through a symmetry-breaking mechanism that is used to learn position displacements with lower symmetry than the input molecule while preserving the rotation equivariance of the charge density itself. Inspired by recent successes of Gaussian Splatting in representing densities in space, we are using Gaussians as our orbitals and predict their weights and covariance matrices. Our method achieves a state-of-the-art balance between computational efficiency and predictive accuracy on established benchmarks.

Related papers

• Hamiltonian truncation tensor networks for quantum field theories [42.2225785045544]
  We introduce a tensor network method for the classical simulation of continuous quantum field theories. The method is built on Hamiltonian truncation and tensor network techniques. One of the key developments is the exact construction of matrix product state representations of global projectors.
  arXiv  Detail & Related papers  (2023-12-19T19:00:02Z)
• Natural orbitals and sparsity of quantum mutual information [0.0]
  We show that the converged orbitals are coinciding with natural orbitals. The correlation is encoded in a smaller number of qubit pairs contributing to the quantum mutual information matrix.
  arXiv  Detail & Related papers  (2023-08-15T21:51:53Z)
• Resolving nonclassical magnon composition of a magnetic ground state via a qubit [44.99833362998488]
  We show that a direct dispersive coupling between a qubit and a noneigenmode magnon enables detecting the magnonic number states' quantum superposition. This unique coupling is found to enable control over the equilibrium magnon squeezing and a deterministic generation of squeezed even Fock states.
  arXiv  Detail & Related papers  (2023-06-08T09:30:04Z)
• Machine learning in and out of equilibrium [58.88325379746631]
  Our study uses a Fokker-Planck approach, adapted from statistical physics, to explore these parallels. We focus in particular on the stationary state of the system in the long-time limit, which in conventional SGD is out of equilibrium. We propose a new variation of Langevin dynamics (SGLD) that harnesses without replacement minibatching.
  arXiv  Detail & Related papers  (2023-06-06T09:12:49Z)
• Message-Passing Neural Quantum States for the Homogeneous Electron Gas [41.94295877935867]
  We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. We demonstrate its accuracy by simulating the ground state of the homogeneous electron gas in three spatial dimensions.
  arXiv  Detail & Related papers  (2023-05-12T04:12:04Z)
• Disentangling Interacting Systems with Fermionic Gaussian Circuits: Application to Quantum Impurity Models [0.0]
  We introduce a change of basis with unitary gates obtained from compressing fermionic Gaussian states into quantum circuits corresponding to various tensor networks.<n>These circuits can reduce the ground state entanglement entropy and improve the performance of algorithms such as the density matrix renormalization group.
  arXiv  Detail & Related papers  (2022-12-19T19:11:16Z)
• Orthogonally Constrained Orbital Optimization: assessing changes of optimal orbitals for orthogonal multi-reference states [0.0]
  The choice of molecular orbitals is decisive in configuration interaction calculations. The approach faithfully recovers the energy of afour-electron Hubbard trimer, whereas state-average calculations can miss the value by a factor 2.5.
  arXiv  Detail & Related papers  (2022-11-15T17:39:39Z)
• Determining ground-state phase diagrams on quantum computers via a generalized application of adiabatic state preparation [61.49303789929307]
  We use a local adiabatic ramp for state preparation to allow us to directly compute ground-state phase diagrams on a quantum computer via time evolution. We are able to calculate an accurate phase diagram on both two and three site systems using IBM quantum machines.
  arXiv  Detail & Related papers  (2021-12-08T23:59:33Z)
• Natural orbitals for the ab initio no-core configuration interaction approach [0.13999481573773068]
  We seek to improve the accuracy obtained for a given basis size by optimizing the choice of single-particle orbitals. Natural orbitals, which diagonalize the one-body density matrix, provide a basis which maximizes the occupation of low-lying orbitals. We explore aspects of NCCI calculations with natural orbitals for the ground state of the $p$-shell neutron halo nucleus.
  arXiv  Detail & Related papers  (2021-12-07T22:38:39Z)
• Quantum Solvers for Plane-Wave Hamiltonians: Abridging Virtual Spaces Through the Optimization of Pairwise Correlations [0.2462953128215087]
  We develop new classes of algorithms to define virtual spaces by optimizing orbitals from small pairwise CI Hamiltonians. Using these derived basis sets for quantum computing calculations targeting full CI (FCI) quality-results can also be used in many-body approaches.
  arXiv  Detail & Related papers  (2020-08-31T19:55:48Z)
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features [42.96944345045462]
  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.
  arXiv  Detail & Related papers  (2020-07-15T22:38:41Z)
• 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.