eQE 2.0: Subsystem DFT Beyond GGA Functionals
- URL: http://arxiv.org/abs/2103.07556v1
- Date: Fri, 12 Mar 2021 22:26:36 GMT
- Title: eQE 2.0: Subsystem DFT Beyond GGA Functionals
- Authors: Wenhui Mi, Xuecheng Shao, Alessandro Genova, Davide Ceresoli, Michele
Pavanello
- Abstract summary: subsystem-DFT (sDFT) can dramatically reduce the computational cost of large-scale electronic structure calculations.
The key ingredients of sDFT are the nonadditive kinetic energy and exchange-correlation functionals which dominate it's accuracy.
eQE 2.0 delivers excellent interaction energies compared to conventional Kohn-Sham DFT and CCSD(T)
- Score: 58.720142291102135
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: By adopting a divide-and-conquer strategy, subsystem-DFT (sDFT) can
dramatically reduce the computational cost of large-scale electronic structure
calculations. The key ingredients of sDFT are the nonadditive kinetic energy
and exchange-correlation functionals which dominate it's accuracy. Even though,
semilocal nonadditive functionals find a broad range of applications, their
accuracy is somewhat limited especially for those systems where achieving
balance between exchange-correlation interactions on one side and nonadditive
kinetic energy on the other is crucial. In eQE 2.0, we improve dramatically the
accuracy of sDFT simulations by (1) implementing nonlocal nonadditive kinetic
energy functionals based on the LMGP family of functionals; (2) adapting
Quantum ESPRESSO's implementation of rVV10 and vdW-DF nonlocal
exchange-correlation functionals to be employed in sDFT simulations; (3)
implementing "deorbitalized" meta GGA functionals (e.g., SCAN-L). We carefully
assess the performance of the newly implemented tools on the S22-5 test set.
eQE 2.0 delivers excellent interaction energies compared to conventional
Kohn-Sham DFT and CCSD(T). The improved performance does not come at a loss of
computational efficiency. We show that eQE 2.0 with nonlocal nonadditive
functionals retains the same linear scaling behavior achieved in eQE 1.0 with
semilocal nonadditive functionals.
Related papers
- AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer [54.713778961605115]
Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks in the computer vision community.
We propose a novel non-uniform quantizer, dubbed the Adaptive Logarithm AdaLog (AdaLog) quantizer.
arXiv Detail & Related papers (2024-07-17T18:38:48Z) - Grad DFT: a software library for machine learning enhanced density
functional theory [0.0]
Density functional theory (DFT) stands as a cornerstone in computational quantum chemistry and materials science.
Recent work has begun to explore how machine learning can expand the capabilities of DFT.
We present Grad DFT: a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange-correlation energy functionals.
arXiv Detail & Related papers (2023-09-23T00:25:06Z) - Variational principle to regularize machine-learned density functionals:
the non-interacting kinetic-energy functional [0.0]
We propose a new and efficient regularization method to train density functionals based on deep neural networks.
The method is tested on (effectively) one-dimensional systems, including the hydrogen chain, non-interacting electrons, and atoms of the first two periods.
For the atomic systems, the generalizability of the regularization method is demonstrated by training also an exchange--correlation functional.
arXiv Detail & Related papers (2023-06-30T12:07:26Z) - Robust Learning with Progressive Data Expansion Against Spurious
Correlation [65.83104529677234]
We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features.
Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process.
We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
arXiv Detail & Related papers (2023-06-08T05:44:06Z) - 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) - HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer
Compression [69.36555801766762]
We propose a hardware-aware tensor decomposition framework, dubbed HEAT, that enables efficient exploration of the exponential space of possible decompositions.
We experimentally show that our hardware-aware factorized BERT variants reduce the energy-delay product by 5.7x with less than 1.1% accuracy loss.
arXiv Detail & Related papers (2022-11-30T05:31:45Z) - NeuralNEB -- Neural Networks can find Reaction Paths Fast [7.7365628406567675]
Quantum mechanical methods like Density Functional Theory (DFT) are used with great success alongside efficient search algorithms for studying kinetics of reactive systems.
Machine Learning (ML) models have turned out to be excellent emulators of small molecule DFT calculations and could possibly replace DFT in such tasks.
In this paper we train state of the art equivariant Graph Neural Network (GNN)-based models on around 10.000 elementary reactions from the Transition1x dataset.
arXiv Detail & Related papers (2022-07-20T15:29:45Z) - Density Functional Theory Transformed into a One-electron Reduced
Density Matrix Functional Theory for the Capture of Static Correlation [0.0]
Density functional theory (DFT) fails to describe accurately the electronic structure of strongly correlated systems.
We show that DFT can be transformed into a one-electron reduced-density-matrix (1-RDM) functional theory.
Our approach yields substantial improvements over traditional DFT in the description of static correlation in chemical structures and processes.
arXiv Detail & Related papers (2022-01-11T01:41:53Z) - GGA-Level Subsystem DFT Achieves Sub-kcal/mol Accuracy Intermolecular
Interactions by Mimicking Nonlocal Functionals [0.0]
We propose a GGA nonadditive kinetic energy functional which mimics the good behavior of nonlocal functionals.
The new functional reproduces Kohn-Sham DFT and benchmark CCSD(T) interaction energies of weakly interacting dimers in the S22-5 and S66 test sets.
arXiv Detail & Related papers (2021-03-29T19:56:52Z) - DFTpy: An efficient and object-oriented platform for orbital-free DFT
simulations [55.41644538483948]
In this work, we present DFTpy, an open source software implementing OFDFT written entirely in Python 3.
We showcase the electronic structure of a million-atom system of aluminum metal which was computed on a single CPU.
DFTpy is released under the MIT license.
arXiv Detail & Related papers (2020-02-07T19:07:41Z)
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.