Capturing long-range interaction with reciprocal space neural network

• URL: http://arxiv.org/abs/2211.16684v1
• Date: Wed, 30 Nov 2022 02:10:48 GMT
• Title: Capturing long-range interaction with reciprocal space neural network
• Authors: Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong, Hongjun Xiang
• Abstract summary: Long-range effects such as Coulomb and Van der Wales potential are not considered in most Machine Learning (ML) interatomic models. Our work has expanded the ability of current ML interatomic models and potentials when dealing with the long-range effect.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning (ML) interatomic models and potentials have been widely employed in simulations of materials. Long-range interactions often dominate in some ionic systems whose dynamics behavior is significantly influenced. However, the long-range effect such as Coulomb and Van der Wales potential is not considered in most ML interatomic potentials. To address this issue, we put forward a method that can take long-range effects into account for most ML local interatomic models with the reciprocal space neural network. The structure information in real space is firstly transformed into reciprocal space and then encoded into a reciprocal space potential or a global descriptor with full atomic interactions. The reciprocal space potential and descriptor keep full invariance of Euclidean symmetry and choice of the cell. Benefiting from the reciprocal-space information, ML interatomic models can be extended to describe the long-range potential including not only Coulomb but any other long-range interaction. A model NaCl system considering Coulomb interaction and the GaxNy system with defects are applied to illustrate the advantage of our approach. At the same time, our approach helps to improve the prediction accuracy of some global properties such as the band gap where the full atomic interaction beyond local atomic environments plays a very important role. In summary, our work has expanded the ability of current ML interatomic models and potentials when dealing with the long-range effect, hence paving a new way for accurate prediction of global properties and large-scale dynamic simulations of systems with defects.

Related papers

• Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution [1.6112718683989882]
  Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. We develop a new equivariant MLIP incorporating long-range Coulomb interactions through explicit treatment of electronic degrees of freedom. We systematically evaluate our model across a range of benchmark periodic and non-periodic datasets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions.
  arXiv  Detail & Related papers  (2025-03-23T05:26:55Z)
• Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
  electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation. This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
  arXiv  Detail & Related papers  (2024-12-17T15:05:33Z)
• Scalable spin squeezing from critical slowing down in short-range interacting systems [0.0]
  We show that scalable squeezing can be produced in 2d U(1)-symmetric systems even by short-range interactions. Our results open the path to realizing massive entangled states of potential metrological interest in many relevant platforms of quantum simulation and information processing.
  arXiv  Detail & Related papers  (2024-04-18T21:29:43Z)
• Inferring Relational Potentials in Interacting Systems [56.498417950856904]
  We propose Neural Interaction Inference with Potentials (NIIP) as an alternative approach to discover such interactions. NIIP assigns low energy to the subset of trajectories which respect the relational constraints observed. It allows trajectory manipulation, such as interchanging interaction types across separately trained models, as well as trajectory forecasting.
  arXiv  Detail & Related papers  (2023-10-23T00:44:17Z)
• Learning Interatomic Potentials at Multiple Scales [1.2162698943818964]
  The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. This work introduces a method to learn a scale separation in complex interatomic interactions by co-training two MLIPs.
  arXiv  Detail & Related papers  (2023-10-20T18:34:32Z)
• 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)
• Slow semiclassical dynamics of a two-dimensional Hubbard model in disorder-free potentials [77.34726150561087]
  We show that introduction of harmonic and spin-dependent linear potentials sufficiently validates fTWA for longer times. In particular, we focus on a finite two-dimensional system and show that at intermediate linear potential strength, the addition of a harmonic potential and spin dependence of the tilt, results in subdiffusive dynamics.
  arXiv  Detail & Related papers  (2022-10-03T16:51:25Z)
• Real space Mott-Anderson electron localization with long-range interactions: exact and approximate descriptions [0.0]
  This work investigates a real-space one-dimensional model of interacting electrons in the presence of a disordered potential. The transition between delocalized and localized phases are characterized using two different indicators. The performance of density functional approximations to reproduce the exact ground-state densities of this many-body localization model are gauged.
  arXiv  Detail & Related papers  (2022-08-30T21:35:46Z)
• Machine learning predictions for local electronic properties of disordered correlated electron systems [2.984639473379942]
  We present a scalable machine learning (ML) model to predict local electronic properties. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems. Our work underscores the promising potential of ML methods for multi-scale modeling of correlated electron systems.
  arXiv  Detail & Related papers  (2022-04-12T17:28:51Z)
• Tuning long-range fermion-mediated interactions in cold-atom quantum simulators [68.8204255655161]
  Engineering long-range interactions in cold-atom quantum simulators can lead to exotic quantum many-body behavior. Here, we propose several tuning knobs, accessible in current experimental platforms, that allow to further control the range and shape of the mediated interactions.
  arXiv  Detail & Related papers  (2022-03-31T13:32:12Z)
• Temporal entanglement, quasiparticles and the role of interactions [0.0]
  We analyze quantum quenches in a family of discrete integrable dynamics corresponding to the real-time Trotterization of the interacting XXZ Heisenberg model. Our findings highlight the non-trivial role of interactions, and raise interesting questions on the possibility to efficiently simulate the local dynamics of interacting integrable systems.
  arXiv  Detail & Related papers  (2021-12-28T19:01:20Z)
• Spin Entanglement and Magnetic Competition via Long-range Interactions in Spinor Quantum Optical Lattices [62.997667081978825]
  We study the effects of cavity mediated long range magnetic interactions and optical lattices in ultracold matter. We find that global interactions modify the underlying magnetic character of the system while introducing competition scenarios. These allow new alternatives toward the design of robust mechanisms for quantum information purposes.
  arXiv  Detail & Related papers  (2020-11-16T08:03:44Z)
• 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.