Ab initio electron-lattice downfolding: potential energy landscapes, anharmonicity, and molecular dynamics in charge density wave materials

• URL: http://arxiv.org/abs/2303.07261v3
• Date: Tue, 16 Jan 2024 14:38:46 GMT
• Title: Ab initio electron-lattice downfolding: potential energy landscapes, anharmonicity, and molecular dynamics in charge density wave materials
• Authors: Arne Schobert, Jan Berges, Erik G. C. P. van Loon, Michael A. Sentef, Sergey Brener, Mariana Rossi, and Tim O. Wehling
• Abstract summary: Computational challenges arise especially for large systems, long time scales, in nonequilibrium, or in systems with strong correlations. We show how downfolding approaches facilitate complexity reduction on the electronic side and thereby boost the simulation of electronic properties and nuclear motion.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The interplay of electronic and nuclear degrees of freedom presents an outstanding problem in condensed matter physics and chemistry. Computational challenges arise especially for large systems, long time scales, in nonequilibrium, or in systems with strong correlations. In this work, we show how downfolding approaches facilitate complexity reduction on the electronic side and thereby boost the simulation of electronic properties and nuclear motion - in particular molecular dynamics (MD) simulations. Three different downfolding strategies based on constraining, unscreening, and combinations thereof are benchmarked against full density functional calculations for selected charge density wave (CDW) systems, namely 1H-TaS$_2$, 1T-TiSe$_2$, 1H-NbS$_2$, and a one-dimensional carbon chain. We find that the downfolded models can reproduce potential energy surfaces on supercells accurately and facilitate computational speedup in MD simulations by about five orders of magnitude in comparison to purely ab initio calculations. For monolayer 1H-TaS$_2$ we report classical replica exchange and quantum path integral MD simulations, revealing the impact of thermal and quantum fluctuations on the CDW transition.

Related papers

• Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations [4.94903410489486]
  phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics. We leverage modern machine learning (ML) methods to achieve a linear-scaling algorithm for simulating the coarsening of charge density waves (CDWs) Our study provides fresh insights into the role of electron correlations in non-equilibrium dynamics and underscores the promise of ML force-field approaches.
  arXiv  Detail & Related papers  (2024-12-30T16:44:11Z)
• The light-matter correlation energy functional of the cavity-coupled two-dimensional electron gas via quantum Monte Carlo simulations [10.311466600238516]
  We show that a modified version of weak-coupling theory is remarkably accurate for a large parameter region. Results provide a numerical foundation for the development of the QED density functional theory.
  arXiv  Detail & Related papers  (2024-12-26T14:03:03Z)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Thermalization and Criticality on an Analog-Digital Quantum Simulator [133.58336306417294]
  We present a quantum simulator comprising 69 superconducting qubits which supports both universal quantum gates and high-fidelity analog evolution. We observe signatures of the classical Kosterlitz-Thouless phase transition, as well as strong deviations from Kibble-Zurek scaling predictions. We digitally prepare the system in pairwise-entangled dimer states and image the transport of energy and vorticity during thermalization.
  arXiv  Detail & Related papers  (2024-05-27T17:40:39Z)
• Orbital-Free Density Functional Theory with Continuous Normalizing Flows [54.710176363763296]
  Orbital-free density functional theory (OF-DFT) provides an alternative approach for calculating the molecular electronic energy. Our model successfully replicates the electronic density for a diverse range of chemical systems.
  arXiv  Detail & Related papers  (2023-11-22T16:42:59Z)
• CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling [0.6860131654491484]
  We present the Crystal Hamiltonian Graph neural Network (CHGNet) as a novel machine-learning interatomic potential (MLIP) CHGNet is pretrained on the energies, forces, stresses, and magnetic moments from the Materials Project Trajectory dataset. We provide new insights into ionic systems with additional electronic degrees of freedom that can not be observed by previous MLIPs.
  arXiv  Detail & Related papers  (2023-02-28T01:30:06Z)
• Studying chirality imbalance with quantum algorithms [62.997667081978825]
  We employ the (1+1) dimensional Nambu-Jona-Lasinio (NJL) model to study the chiral phase structure and chirality charge density of strongly interacting matter. By performing the Quantum imaginary time evolution (QITE) algorithm, we simulate the (1+1) dimensional NJL model on the lattice at various temperature $T$ and chemical potentials $mu$, $mu_5$.
  arXiv  Detail & Related papers  (2022-10-06T17:12:33Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Machine learning dynamics of phase separation in correlated electron magnets [0.0]
  We demonstrate machine-learning enabled large-scale dynamical simulations of electronic phase separation in double-exchange system. Our work paves the way for large-scale dynamical simulations of correlated electron systems using machine-learning models.
  arXiv  Detail & Related papers  (2020-06-07T17:01:06Z)
• Resource Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and The Double Unitary Coupled-Cluster approach [0.0]
  We show that the number of qubits scales linearly with the size of molecular basis. We employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces.
  arXiv  Detail & Related papers  (2020-04-16T15:59:15Z)
• Entanglement generation via power-of-SWAP operations between dynamic electron-spin qubits [62.997667081978825]
  Surface acoustic waves (SAWs) can create moving quantum dots in piezoelectric materials. We show how electron-spin qubits located on dynamic quantum dots can be entangled.
  arXiv  Detail & Related papers  (2020-01-15T19:00:01Z)

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.