Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

• URL: http://arxiv.org/abs/2010.04905v2
• Date: Fri, 9 Jul 2021 08:18:02 GMT
• Title: Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
• Authors: J. Austin Ellis and Lenz Fiedler and Gabriel A. Popoola and Normand A. Modine and J. Adam Stephens and Aidan P. Thompson and Attila Cangi and Sivasankaran Rajamanickam
• Abstract summary: We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum.
• Score: 2.7035666571881856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

Related papers

• Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties [6.875235178607604]
  We introduce a neuroevolution potential (NEP) trained on a dataset generated from ab initio molecular dynamics (AIMD) simulations. We calculate the phonon density of states (DOS) and radial distribution function (RDF) using both machine learning potentials. While the MTP potential offers slightly higher accuracy, the NEP achieves a remarkable 41-fold increase in computational speed.
  arXiv  Detail & Related papers  (2024-11-16T23:16:59Z)
• 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)
• DimOL: Dimensional Awareness as A New 'Dimension' in Operator Learning [63.5925701087252]
  We introduce DimOL (Dimension-aware Operator Learning), drawing insights from dimensional analysis. To implement DimOL, we propose the ProdLayer, which can be seamlessly integrated into FNO-based and Transformer-based PDE solvers. Empirically, DimOL models achieve up to 48% performance gain within the PDE datasets.
  arXiv  Detail & Related papers  (2024-10-08T10:48:50Z)
• NeuralSCF: Neural network self-consistent fields for density functional theory [1.7667864049272723]
  Kohn-Sham density functional theory (KS-DFT) has found widespread application in accurate electronic structure calculations. We propose a neural network self-consistent fields (NeuralSCF) framework that establishes the Kohn-Sham density map as a deep learning objective.
  arXiv  Detail & Related papers  (2024-06-22T15:24:08Z)
• 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)
• 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)
• D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
  We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT) We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity. In addition, we show that our approach enables us to explore more complex neural-based wave functions.
  arXiv  Detail & Related papers  (2023-03-01T10:38:10Z)
• 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)
• Computing molecular excited states on a D-Wave quantum annealer [52.5289706853773]
  We demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.
  arXiv  Detail & Related papers  (2021-07-01T01:02:17Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)
• DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction [0.0]
  DeepDFT is a deep learning model for predicting the electronic charge density around atoms. The accuracy and scalability of the model are demonstrated for molecules, solids and liquids.
  arXiv  Detail & Related papers  (2020-11-04T16:56:08Z)

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.