Quantum-informed simulations for mechanics of materials: DFTB+MBD framework

• URL: http://arxiv.org/abs/2404.04216v1
• Date: Fri, 5 Apr 2024 16:59:01 GMT
• Title: Quantum-informed simulations for mechanics of materials: DFTB+MBD framework
• Authors: Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas, Alexandre Tkatchenko, Jakub Lengiewicz,
• Abstract summary: We study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We provide an open-source repository containing all codes, datasets, and examples presented in this work.
• Score: 40.83978401377059
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and understanding these behaviors require models that rely on insights from electronic and atomic scales. In such cases, classical simplified approximations at those scales are insufficient, and quantum-based modeling is required. In this paper, we study how quantum effects can modify the mechanical properties of systems relevant to materials engineering. We base our study on a high-fidelity modeling framework that combines two computationally efficient models rooted in quantum first principles: Density Functional Tight Binding (DFTB) and many-body dispersion (MBD). The MBD model is applied to accurately describe non-covalent van der Waals interactions. Through various benchmark applications, we demonstrate the capabilities of this framework and the limitations of simplified modeling. We provide an open-source repository containing all codes, datasets, and examples presented in this work. This repository serves as a practical toolkit that we hope will support the development of future research in effective large-scale and multiscale modeling with quantum-mechanical fidelity.

Related papers

• Bayesian Modelling Approaches for Quantum States -- The Ultimate Gaussian Process States Handbook [0.0]
  This thesis discusses novel tools and techniques for the (classical) modelling of quantum many-body wavefunctions. It is outlined how synergies with standard machine learning approaches can be exploited to enable an automated inference of the most relevant intrinsic characteristics. The resulting model carries a high degree of interpretability and offers an easily applicable tool for study of quantum systems.
  arXiv  Detail & Related papers  (2023-08-15T09:37:58Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Calibrating constitutive models with full-field data via physics informed neural networks [0.0]
  We propose a physics-informed deep-learning framework for the discovery of model parameterizations given full-field displacement data. We work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. We demonstrate that informed machine learning is an enabling technology and may shift the paradigm of how full-field experimental data is utilized to calibrate models.
  arXiv  Detail & Related papers  (2022-03-30T18:07:44Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Quantum Model Learning Agent: characterisation of quantum systems through machine learning [0.6474760227870044]
  We report an algorithm -- the Quantum Model Learning Agent (QMLA) -- to reverse engineer Hamiltonian descriptions of a target system. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models.
  arXiv  Detail & Related papers  (2021-12-15T19:01:53Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• A Universal Framework for Featurization of Atomistic Systems [0.0]
  Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales. We introduce the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms. We demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements.
  arXiv  Detail & Related papers  (2021-02-04T03:11:00Z)
• Variational classical networks for dynamics in interacting quantum matter [0.0]
  We introduce a variational class of wavefunctions based on complex networks of classical spins akin to artificial neural networks. We show that our method can be applied to any quantum many-body system with a well-defined classical limit.
  arXiv  Detail & Related papers  (2020-07-31T14:03:37Z)
• 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)
• Exact representations of many body interactions with RBM neural networks [77.34726150561087]
  We exploit the representation power of RBMs to provide an exact decomposition of many-body contact interactions into one-body operators. This construction generalizes the well known Hirsch's transform used for the Hubbard model to more complicated theories such as Pionless EFT in nuclear physics.
  arXiv  Detail & Related papers  (2020-05-07T15:59:29Z)

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.