Time Dependent DFT in Tamm-Dancoff Approximation

Overview

image

Density Function Theory Program - kspy-tddft(tda)

This is an implementation of Time-Dependent Density Functional Theory(TDDFT) using the Tamm-Dancoff Approximation (TDA).

The Grid

I could have used a cube as a space grid and then taken Riemann sums to evaluate (there's a great YouTube series by James Johns where he develops a matlab HF program and shows how to convert it to DFT. In this he uses Riemann sums to evaluate the integrals in the DFT program.) However, I decided to try for a 'proper' atom centered spherical grid approach. A useful paper was PMW Gill, BG Johnson and JA Poples 'A standard grid for density functional theory', although I didn't use this SG-1 grid the paper helped understand the techniques involved. The grid I settled on was a coarse grid of (10,15) radial points for period 1 and period 2 elements respectively. The radial grid is a Mura-Knowles radial grid ME Mura and PJ Knowles 'Improved radial grids for quadrature in density-functional calculations' JCP 104, 9848 (1996); DOI:10.1063/1.471749. The 'coarse' angular grid is of Lebedev orders (11, 15) for period 1 and period 2 respectively. This translates into 50 and 86 points respectively arranged on a spherical shell (VI Lebedev, and DN Laikov, Doklady Mathematics, 'A Quadrature formula for the sphere of the 131st algebraic order of accuracy' Vol. 59, No. 3, (1999)). There are various sources for this data given in the external links of the wikipedia article on Lebedev integration. A pruning scheme is employed to systematically reduce the number of angular points in regions where dense angular quadrature is not necessary, such as near the nuclei where the charge density is approximately spherically symmetric and at long distance from the nucleus. The pruning scheme I employed was the Treutler-Aldrich scheme O Treutler and R Ahlrich, 'Efficient molecular numerical integration schemes' JCP 102, 346 (1995); DOI:10.1063/1.469408. The partitioning of the atomic centered grids to a molecular grid follows a Becke scheme after Stratmann RE Stratmann, GE Scuseria and MJ Frisch, 'Achieving Linear scaling in exchange-correlation density functional quadratures' CPL 257, 3-4 (1996); DOI:10.1016/009-2614(96)00600-8. Finally I have implemented a final radius adjustment during the partition (Becke suggests doing this) using the Bragg radius. A second 'close' grid is also included which is a (50, 75) radial and (29, 29) angular, the latter representing 302 points on each shell. The grid routines are in ks_grid.py.

The HF Integrals

To get the DFT SCF started we need an initial density. To do this I use a HF overlap matrix S, and an initial Fock matrix composed of the sum of the 1-electron kinetic and coulomb integrals (core Hamiltonian - T+V). This Fock is then orthogonalised (F') as (S-0.5)TFS-0.5, eigensolve the resulting orthogonal Fock for orbital coefficients C orthogonal, transform back to atomic basis as S-0.5C', use resulting ao coefficients to compute a density matrix Dμν = cμic where i is over occupied orbitals. This initial density can be used with initial Fock and 2-electron repulsion integrals to form the coulomb integral J (we don't want the HF exchange integral K for DFT). To get these integrals I've used a modified version of Harpy's Cython integral package aello. This version is slightly different from the version in kspy_lda in that the dipole routine returns the component matrices rather than the actual dipole, additionally the angular and nabla routines have been added. These are in ks_aello.pyx.

Molecule and Basis Sets

The molecule definition is contained in a mol object which is itself comprised of objects from an atom class. Each instance of the atom class contains the atom symbol, atomic number and the coordinates of the atom center (array[3]). The molecule is hard coded as H2O. The basis is contained in an orb object which is itself comprised of objects from a gaussian class. Each instance of the gaussian class contains the atom the Gaussian is centered on, the momentum(array[3]), the exponents (array[primatives], the coefficients (array[primatives]), the normalisation (array[primatives]) and a copy of the atom center coordinates (array[3]). The momenta are given as s [0,0,0] px [1,0,0] py [0,1,0] and pz [0,0,1]. The basis used is a simple STO-3G so we only require s and p orbitals. The primatives exponent and coefficient values are hard-coded in the main section. (I use the psi4 format of the basis sets from BSE which have some (small) differences from the nwchem format versions as used by eg pyscf. This might lead to numerical differences in values when using high precision).

The Functionals

The choice of functionals here was determined solely because htey have easily determined analytic derivatives. The second derivatives of the exchange-correlation energy are needed in TDDFT to determine the orbital Hessian for the coupling matrix. The exchange functional is Slater LDA and the correlation functional is RPA. For TDDFT we are working in a molecular spin basis so will use spin polarized versions of the functional (with α = β). The derivatives used are given below

image

TDDFT

Details of TDDFT can be found in Time-dependent density-functional theory for molecules and molecular solids, ME Casida, Journal of Molecular Structure: THEOCHEM 914 (2009) 3–18 and Single-Reference ab Initio Methods for the Calculation of Excited States of Large Molecules, A Dreuw and M Head-Gordon, Chem. Rev.2005,105,4009−4037. The basic equations for the excitation (de-excitation) energies are image

The calculation of singlet and triplet states is analogous to the spin-adapted CIS calculation in HF theory see. The class TDA is provided to compute the excitation energies and coefficients for either singlet or triplet states. The class provides a response property which is a tuple (energy, coefficients).

Response Properties

An example of calculating transition properties in TDDFT is given in this psicon 2020 document. We calculate the electric transition dipoles in both length (μ) and velocity (∇) gauges together with the associated oscillator strengths. Additionally the magnetic transition dipoles are calculated in the length (L) gauge and the rotary strengths in both gauges. All the previous properties are returned by the transition_properties method of the TDA_properties class.
A basic transition natural orbital treatment is available from the transition_NO method of the TDA_properties class. Martin, R. L., Journal of Chemical Physics, 118, 4775-4777.
A spectrum method of the TDA_properties class is provided to plot the spectrum of oscillator strengths in both gauges. This is all provided in the module ks_tda. See results.md for more details.

Owner
Peter Borthwick
Retired. M.Sc Mathematics (Kings', London), Ph.D in theoretical chemistry.
Peter Borthwick
Data Consistency for Magnetic Resonance Imaging

Data Consistency for Magnetic Resonance Imaging Data Consistency (DC) is crucial for generalization in multi-modal MRI data and robustness in detectin

Dimitris Karkalousos 19 Dec 12, 2022
WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution

WPPNets: Unsupervised CNN Training with Wasserstein Patch Priors for Image Superresolution This code belongs to the paper [1] available at https://arx

Fabian Altekrueger 5 Jun 02, 2022
Code for the paper "On the Power of Edge Independent Graph Models"

Edge Independent Graph Models Code for the paper: "On the Power of Edge Independent Graph Models" Sudhanshu Chanpuriya, Cameron Musco, Konstantinos So

Konstantinos Sotiropoulos 0 Oct 26, 2021
AgeGuesser: deep learning based age estimation system. Powered by EfficientNet and Yolov5

AgeGuesser AgeGuesser is an end-to-end, deep-learning based Age Estimation system, presented at the CAIP 2021 conference. You can find the related pap

5 Nov 10, 2022
PyTorch Implementation of "Light Field Image Super-Resolution with Transformers"

LFT PyTorch implementation of "Light Field Image Super-Resolution with Transformers", arXiv 2021. [pdf]. Contributions: We make the first attempt to a

Squidward 62 Nov 28, 2022
Speech Recognition using DeepSpeech2.

deepspeech.pytorch Implementation of DeepSpeech2 for PyTorch using PyTorch Lightning. The repo supports training/testing and inference using the DeepS

Sean Naren 2k Jan 04, 2023
Machine Learning with JAX Tutorials

The purpose of this repo is to make it easy to get started with JAX. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I fou

Aleksa Gordić 372 Dec 28, 2022
Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

🔍 Watermarking Images in Self-Supervised Latent-Spaces PyTorch implementation and pretrained models for the paper. For details, see Watermarking Imag

Meta Research 32 Dec 13, 2022
DrQ-v2: Improved Data-Augmented Reinforcement Learning

DrQ-v2: Improved Data-Augmented RL Agent Method DrQ-v2 is a model-free off-policy algorithm for image-based continuous control. DrQ-v2 builds on DrQ,

Facebook Research 234 Jan 01, 2023
Tool for live presentations using manim

manim-presentation Tool for live presentations using manim Install pip install manim-presentation opencv-python Usage Use the class Slide as your sce

Federico Galatolo 146 Jan 06, 2023
A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking

PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking PoseRBPF Paper Self-supervision Paper Pose Estimation Video Robot Manipulati

NVIDIA Research Projects 107 Dec 25, 2022
Random Walk Graph Neural Networks

Random Walk Graph Neural Networks This repository is the official implementation of Random Walk Graph Neural Networks. Requirements Code is written in

Giannis Nikolentzos 38 Jan 02, 2023
Fully Convolutional DenseNet (A.K.A 100 layer tiramisu) for semantic segmentation of images implemented in TensorFlow.

FC-DenseNet-Tensorflow This is a re-implementation of the 100 layer tiramisu, technically a fully convolutional DenseNet, in TensorFlow (Tiramisu). Th

Hasnain Raza 121 Oct 12, 2022
A self-supervised learning framework for audio-visual speech

AV-HuBERT (Audio-Visual Hidden Unit BERT) Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction Robust Self-Supervised A

Meta Research 431 Jan 07, 2023
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
Repository for "Exploring Sparsity in Image Super-Resolution for Efficient Inference", CVPR 2021

SMSR Reposity for "Exploring Sparsity in Image Super-Resolution for Efficient Inference" [arXiv] Highlights Locate and skip redundant computation in S

Longguang Wang 225 Dec 26, 2022
WatermarkRemoval-WDNet-WACV2021

WatermarkRemoval-WDNet-WACV2021 Thank you for your attention. Citation Please cite the related works in your publications if it helps your research: @

LUYI 63 Dec 05, 2022
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision

MLP Mixer Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. Give us a star if you like this repo. Author: Github: bangoc123 Emai

Ngoc Nguyen Ba 86 Dec 10, 2022
MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system

MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system Getting started To start working on this assignment, you should

2 Aug 06, 2022
Implementation of Shape and Electrostatic similarity metric in deepFMPO.

DeepFMPO v3D Code accompanying the paper "On the value of using 3D-shape and electrostatic similarities in deep generative methods". The paper can be

34 Nov 28, 2022