Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation

• URL: http://arxiv.org/abs/2010.07116v2
• Date: Mon, 30 Nov 2020 15:22:13 GMT
• Title: Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation
• Authors: Alberto Fabrizio, Ksenia R. Briling, David D. Girardier and Clemence Corminboeuf
• Abstract summary: The on-top pair density is a powerful indicator of electron correlation effects. A machine learning model capable of predicting CF ontop density molecules only its structure composition is trained on the GDB11AD-3165 database. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bondbreaking fit in realspace.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The on-top pair density [$\Pi(\mathrm{\mathbf{r}})$] is a local quantum-chemical property that reflects the probability of two electrons of any spin to occupy the same position in space. Being the simplest quantity related to the two-particle density matrix, the on-top pair density is a powerful indicator of electron correlation effects, and as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of $\Pi(\mathrm{\mathbf{r}})$ is currently hindered by the need for post-Hartree--Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the CASSCF-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules, bypassing completely the need for $\textit{ab initio}$ computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density.

Related papers

• 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)
• KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory [13.437597619451568]
  KineticNet is an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. For the first time, chemical accuracy of the learned functionals is achieved across input densities and geometries of tiny molecules.
  arXiv  Detail & Related papers  (2023-05-08T17:43:31Z)
• Electronic-structure properties from atom-centered predictions of the electron density [0.0]
  electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. We propose a gradient-based approach to minimize the loss function of the regression problem in an optimized and highly sparse feature space. We show that starting from the predicted density a single Kohn-Sham diagonalization step can be performed to access total energy components that carry an error of just 0.1 meV/atom.
  arXiv  Detail & Related papers  (2022-06-28T15:35:55Z)
• Graph neural networks for fast electron density estimation of molecules, liquids, and solids [0.0]
  We present a machine learning framework for the prediction of $rho(vecr)$. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo (1-y-z)O2 lithium ion battery cathodes (NMC)
  arXiv  Detail & Related papers  (2021-12-01T16:57:31Z)
• Density-Based Clustering with Kernel Diffusion [59.4179549482505]
  A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in density-based clustering algorithms. We propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness.
  arXiv  Detail & Related papers  (2021-10-11T09:00:33Z)
• $\mathcal{P}$,$\mathcal{T}$-odd effects for RaOH molecule in the excited vibrational state [77.34726150561087]
  Triatomic molecule RaOH combines the advantages of laser-coolability and the spectrum with close opposite-parity doublets. We obtain the rovibrational wave functions of RaOH in the ground electronic state and excited vibrational state using the close-coupled equations derived from the adiabatic Hamiltonian.
  arXiv  Detail & Related papers  (2020-12-15T17:08:33Z)
• Too big, too small or just right? A benchmark assessment of density functional theory for predicting the spatial extent of the electron density of small chemical systems [0.0]
  A benchmark dataset of the prinicpal components of $mathcalK$ for 100 small molecules is developed. The performance of 47 popular and recent density functionals is assessed against this dataset.
  arXiv  Detail & Related papers  (2020-11-25T08:00:31Z)
• A multiconfigurational study of the negatively charged nitrogen-vacancy center in diamond [55.58269472099399]
  Deep defects in wide band gap semiconductors have emerged as leading qubit candidates for realizing quantum sensing and information applications. Here we show that unlike single-particle treatments, the multiconfigurational quantum chemistry methods, traditionally reserved for atoms/molecules, accurately describe the many-body characteristics of the electronic states of these defect centers.
  arXiv  Detail & Related papers  (2020-08-24T01:49:54Z)
• 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)
• Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles [3.0980025155565376]
  "MuML" models are fitted together to reproduce molecular $boldsymbolmu$ computed using high-level coupled-cluster theory. We demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model.
  arXiv  Detail & Related papers  (2020-03-27T14:35:37Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

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.