N-representable one-electron reduced density matrix reconstruction with
frozen core electrons
- URL: http://arxiv.org/abs/2403.00534v1
- Date: Fri, 1 Mar 2024 13:48:05 GMT
- Title: N-representable one-electron reduced density matrix reconstruction with
frozen core electrons
- Authors: Sizhuo Yu, Jean-Michel Gillet
- Abstract summary: Recent advances in quantum crystallography have shown that a one-electron reduced density matrix (1-RDM) satisfying N-representability conditions can be reconstructed.
An improved model, including symmetry constraints and frozen-core electron contribution, is introduced to better handle the increasing system complexity.
The robustness of the model and the strategy are shown to be well-adapted to address the reconstruction problem from actual experimental scattering data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in quantum crystallography have shown that, beyond
conventional charge density refinement, a one-electron reduced density matrix
(1-RDM) satisfying N-representability conditions can be reconstructed using
jointly experimental X-ray structure factors (XSF) and directional Compton
profiles (DCP) through semi-definite programming. So far, such reconstruction
methods for 1-RDM, not constrained to idempotency, had been tested only on a
toy model system (CO$_2$). In this work, a new method is assessed on
crystalline urea (CO(NH$_2$)$_2$) using static (0 K) and dynamic (50 K)
artificial-experimental data. An improved model, including symmetry constraints
and frozen-core electron contribution, is introduced to better handle the
increasing system complexity. Reconstructed 1-RDMs, deformation densities and
DCP anisotropy are analyzed, and it is demonstrated that the changes in the
model significantly improve the reconstruction's quality against insufficient
information and data corruption. The robustness of the model and the strategy
are thus shown to be well-adapted to address the reconstruction problem from
actual experimental scattering data.
Related papers
- Recurrent Deep Kernel Learning of Dynamical Systems [0.5825410941577593]
Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets.
We propose a data-driven, non-intrusive deep kernel learning (SVDKL) method to discover low-dimensional latent spaces from data.
Results show that our framework is capable of (i) denoising and reconstructing measurements, (ii) learning compact representations of system states, (iii) predicting system evolution in low-dimensional latent spaces, and (iv) modeling uncertainties.
arXiv Detail & Related papers (2024-05-30T07:49:02Z) - Scalable Diffusion for Materials Generation [99.71001883652211]
We develop a unified crystal representation that can represent any crystal structure (UniMat)
UniMat can generate high fidelity crystal structures from larger and more complex chemical systems.
We propose additional metrics for evaluating generative models of materials.
arXiv Detail & Related papers (2023-10-18T15:49:39Z) - Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction [60.95625458395291]
In computed tomography (CT) the forward model consists of a linear transform followed by an exponential nonlinearity based on the attenuation of light according to the Beer-Lambert Law.
We show that this approach reduces metal artifacts compared to a commercial reconstruction of a human skull with metal crowns.
arXiv Detail & Related papers (2023-10-06T00:47:57Z) - Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution [0.0]
We present a novel data assimilation strategy in pore-scale imaging.
We demonstrate that this makes it possible to robustly address reactive inverse problems incorporating Uncertainty Quantification.
arXiv Detail & Related papers (2023-08-24T15:39:01Z) - Reference-Free Isotropic 3D EM Reconstruction using Diffusion Models [8.590026259176806]
We propose a diffusion-model-based framework that overcomes the limitations of requiring reference data or prior knowledge about the degradation process.
Our approach utilizes 2D diffusion models to consistently reconstruct 3D volumes and is well-suited for highly downsampled data.
arXiv Detail & Related papers (2023-08-03T07:57:02Z) - Knowledge Distillation Performs Partial Variance Reduction [93.6365393721122]
Knowledge distillation is a popular approach for enhancing the performance of ''student'' models.
The underlying mechanics behind knowledge distillation (KD) are still not fully understood.
We show that KD can be interpreted as a novel type of variance reduction mechanism.
arXiv Detail & Related papers (2023-05-27T21:25:55Z) - Solution existence, uniqueness, and stability of discrete basis
sinograms in multispectral CT [4.084909224028198]
This work investigates conditions for quantitative image reconstruction in multispectral computed tomography (MSCT)
An empirical, but highly effective, two-step data-domain-decomposition (DDD) method has been developed and used widely for quantitative image reconstruction in MSCT.
arXiv Detail & Related papers (2023-05-05T07:22:20Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy
CT Reconstruction [108.06731611196291]
We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies.
We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
arXiv Detail & Related papers (2022-03-10T14:22:54Z) - Disentangling multiple scattering with deep learning: application to
strain mapping from electron diffraction patterns [48.53244254413104]
We implement a deep neural network called FCU-Net to invert highly nonlinear electron diffraction patterns into quantitative structure factor images.
We trained the FCU-Net using over 200,000 unique dynamical diffraction patterns which include many different combinations of crystal structures.
Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories.
arXiv Detail & Related papers (2022-02-01T03:53:39Z) - Dynamic fracture of a bicontinuously nanostructured copolymer: A deep
learning analysis of big-data-generating experiment [0.0]
We report the dynamic fracture toughness as well as the cohesive parameters of a bicontinuously nanostructured copolymer, polyurea, under an extremely high crack-tip loading rate.
For the first time, the dynamic cohesive parameters of polyurea have been successfully obtained by the pre-trained CNN architecture.
arXiv Detail & Related papers (2021-12-03T15:31:59Z)
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.