Benchmark Computations of Nearly Degenerate Singlet and Triplet states of N-heterocyclic Chromophores : II. Density-based Methods

• URL: http://arxiv.org/abs/2408.04915v1
• Date: Fri, 9 Aug 2024 07:47:38 GMT
• Title: Benchmark Computations of Nearly Degenerate Singlet and Triplet states of N-heterocyclic Chromophores : II. Density-based Methods
• Authors: Shamik Chanda, Subhasish Saha, Sangita Sen,
• Abstract summary: A set of functionals with the least mean absolute error (MAE) is proposed for both the approaches, LR-TDDFT and $Delta$SCF. We have based our findings on extensive studies of three cyclazine-based molecular templates. The role of exact-exchange, spin-contamination and spin-polarization in the context of DFT comes to the forefront in our studies.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we demonstrate the performance of several density-based methods in predicting the inversion of S$_1$ and T$_1$ states of a few N-heterocyclic fused ring molecules (popularly known as INVEST molecules) with an eye to identify a well performing but cheap preliminary screening method. Both conventional LR-TDDFT and $\Delta$SCF methods (namely MOM, SGM, ROKS) are considered for excited state computations using exchange-correlation (XC) functionals from different rungs of the Jacob's ladder. A well-justified systematism is observed in the performance of the functionals when compared against FICMRCISD and/or EOM-CCSD, with the most important feature being the capture of spin-polarization in presence of correlation. A set of functionals with the least mean absolute error (MAE) is proposed for both the approaches, LR-TDDFT and $\Delta$SCF, which can be cheaper alternatives for computations on synthesizable larger derivatives of the templates studied here. We have based our findings on extensive studies of three cyclazine-based molecular templates, with additional studies on a set of six related templates. Previous benchmark studies for subsets of the functionals were conducted against the DLPNO-STEOM-CCSD, which resulted in an inadequate evaluation due to deficiencies in the benchmark theory. The role of exact-exchange, spin-contamination and spin-polarization in the context of DFT comes to the forefront in our studies and supports the numerical evaluation of XC functionals for these applications. Suitable connections are drawn to two and three state exciton models which identify the minimal physics governing the interactions in these molecules.

Related papers

• Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data [0.0]
  Bi-Directional Long Short Term Memory (BiLSTM) is a variant of Recurrent Neural Network (RNN) that processes input molecular sequences. The proposed work aims to understand the sequential patterns encoded in the SMILES strings, which are then utilised for predicting the toxicity of the molecules.
  arXiv  Detail & Related papers  (2024-07-08T18:12:11Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Chebyshev pseudosite matrix product state approach for cluster perturbation theory [7.629286500184708]
  We introduce the Chebyshev pseudosite matrix product state approach (ChePSMPS) as a solver for cluster theory (CPT) ChePSMPS distinguishes itself from conventional exact diagonalization solvers by supporting larger clusters. We use this method to simulate the spectra for both one- and two-dimensional Hubbard-Holstein models.
  arXiv  Detail & Related papers  (2024-04-08T17:10:11Z)
• SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction [1.534667887016089]
  We present a novel method for generating molecular fingerprints using multi parameter persistent homology (MPPH) This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital. We demonstrate its superior performance over existing state-of-the-art methods in predicting molecular properties through extensive evaluations on the MoleculeNet benchmark.
  arXiv  Detail & Related papers  (2023-12-12T09:33:54Z)
• 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)
• Simulating Spin-Orbit Coupling With Quasidegenerate N-Electron Valence Perturbation Theory [77.34726150561087]
  We present the first implementation of spin-orbit coupling effects in SO-QDNEVPT2. The accuracy of these methods is tested for the group 14 and 16 hydrides, 3d and 4d transition metal ions, and two actinide dioxides.
  arXiv  Detail & Related papers  (2022-11-11T20:03:37Z)
• Equivariant Diffusion for Molecule Generation in 3D [74.289191525633]
  This work introduces a diffusion model for molecule computation generation in 3D that is equivariant to Euclidean transformations. Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time.
  arXiv  Detail & Related papers  (2022-03-31T12:52:25Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Surrogate-based variational data assimilation for tidal modelling [0.0]
  Data assimilation (DA) is widely used to combine physical knowledge and observations. In a context of climate change, old calibrations can not necessarily be used for new scenarios. This raises the question of DA computational cost. Two methods are proposed to replace the complex model by a surrogate.
  arXiv  Detail & Related papers  (2021-06-08T07:39:38Z)
• Heterogeneous Tensor Mixture Models in High Dimensions [5.656785831541303]
  We consider the problem of jointly introducing a flexible high-dimensional tensor mixture model with heterogeneous covariances. We show that our method converges geometrically to a neighborhood that is statistical of the true parameter. Our analysis identifies important brain regions for diagnosis in an autism spectrum disorder.
  arXiv  Detail & Related papers  (2021-04-15T21:06:16Z)
• Lower bounds in multiple testing: A framework based on derandomized proxies [107.69746750639584]
  This paper introduces an analysis strategy based on derandomization, illustrated by applications to various concrete models. We provide numerical simulations of some of these lower bounds, and show a close relation to the actual performance of the Benjamini-Hochberg (BH) algorithm.
  arXiv  Detail & Related papers  (2020-05-07T19:59:51Z)

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.