scholarly journals High Accuracy Semi-Empirical Quantum Models Based on a Reduced Training Set

2021 ◽  
Author(s):  
Cong Huy Pham ◽  
Rebecca Lindsey ◽  
Laurence Fried ◽  
Nir Goldman
Keyword(s):  
Density Functional ◽  
Tight Binding ◽  
Coupled Cluster ◽  
Computationally Efficient ◽  
Learning Potential ◽  
Training Set ◽  
Cluster Accuracy ◽  
High Level ◽  
Physical And Chemical ◽  
Many Body Interactions

There exists a great need for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories while exhibiting a wide degree of transferability. In this regard, we have leveraged a machine-learned force field based on Chebyshev polynomials to determine Density Functional Tight Binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential (ANI- 1x). In addition, the total number of data points in our training set is less than one half of that used for a recent DFTB-neural network model (trained on a separate dataset). Validation tests of our DFTB model against energy and vibrational data for gas-phase molecules for additional quantum datasets shows strong agreement with reference data from either hybrid density-functional theory, coupled-cluster calculations, or experiments. Preliminary testing on graphite and diamond successfully reproduce condensed phase structures. The models developed in this work, in principle, can retain most of the accuracy of quantum-based methods at any level of theory with relatively small training sets. Our efforts can thus allow for high throughput physical and chemical predictions with up to coupled-cluster accuracy for materials that are computationally intractable with standard approaches.

scholarly journals Highly Accurate Many-Body Potentials for Simulations of N2O5 in Water: Benchmarks, Development, and Validation

2021 ◽  
Author(s):  
Vinicius Cruzeiro ◽  
Eleftherios Lambros ◽  
Marc Riera ◽  
Ronak Roy ◽  
Francesco Paesani ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Atmospheric Chemistry ◽  
Short Range ◽  
Density Functional ◽  
Coupled Cluster ◽  
Many Body ◽  
Functional Theory ◽  
High Level ◽  
Three Body ◽  
Many Body Interactions

<div><div><div><p>Dinitrogen pentoxide (N2O5) is an important intermediate in the atmospheric chemistry of nitrogen oxides. Although there has been much research, the processes that govern the physical interactions between N2O5 and water are still not fully understood at a molecular level. Gaining quantitative insight from computer simulations requires going beyond the accuracy of classical force fields, while accessing length scales and time scales that are out of reach for high-level quantum chemical approaches. To this end we present the development of MB-nrg many-body potential energy functions for simulations of N2O5 in water. This MB-nrg model is based on electronic structure calculations at the coupled cluster level of theory and is compatible with the successful MB-pol model for water. It provides a physically correct description of long-range many-body interactions in combination with an explicit representation of up to three-body short-range interactions in terms of multidimensional permutationally invariant polynomials. In order to further investigate the importance of the underlying interactions in the model, a TTM-nrg model was also devised. TTM- nrg is a more simplistic representation that contains only two-body short-range interactions represented through Born-Mayer functions. In this work an active learning approach was employed to efficiently build representative training sets of monomer, dimer and trimer structures, and benchmarks are presented to determine the accuracy of our new models in comparison to a range of density functional theory methods. By assessing binding curves, distortion energies of N2O5, and interaction energies in clusters of N2O5 and water, we evaluate the importance of two-body and three-body short-range potentials. The results demonstrate that our MB-nrg model has high accuracy with respect to the coupled cluster reference, outperforms current density functional theory models, and thus enables highly accurate simulations of N2O5 in aqueous environments.</p></div></div></div>

scholarly journals Highly Accurate Many-Body Potentials for Simulations of N2O5 in Water: Benchmarks, Development, and Validation

2021 ◽  
Author(s):  
Vinicius Cruzeiro ◽  
Eleftherios Lambros ◽  
Marc Riera ◽  
Ronak Roy ◽  
Francesco Paesani ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Atmospheric Chemistry ◽  
Short Range ◽  
Density Functional ◽  
Coupled Cluster ◽  
Many Body ◽  
Functional Theory ◽  
High Level ◽  
Three Body ◽  
Many Body Interactions

<div><div><div><p>Dinitrogen pentoxide (N2O5) is an important intermediate in the atmospheric chemistry of nitrogen oxides. Although there has been much research, the processes that govern the physical interactions between N2O5 and water are still not fully understood at a molecular level. Gaining quantitative insight from computer simulations requires going beyond the accuracy of classical force fields, while accessing length scales and time scales that are out of reach for high-level quantum chemical approaches. To this end we present the development of MB-nrg many-body potential energy functions for simulations of N2O5 in water. This MB-nrg model is based on electronic structure calculations at the coupled cluster level of theory and is compatible with the successful MB-pol model for water. It provides a physically correct description of long-range many-body interactions in combination with an explicit representation of up to three-body short-range interactions in terms of multidimensional permutationally invariant polynomials. In order to further investigate the importance of the underlying interactions in the model, a TTM-nrg model was also devised. TTM- nrg is a more simplistic representation that contains only two-body short-range interactions represented through Born-Mayer functions. In this work an active learning approach was employed to efficiently build representative training sets of monomer, dimer and trimer structures, and benchmarks are presented to determine the accuracy of our new models in comparison to a range of density functional theory methods. By assessing binding curves, distortion energies of N2O5, and interaction energies in clusters of N2O5 and water, we evaluate the importance of two-body and three-body short-range potentials. The results demonstrate that our MB-nrg model has high accuracy with respect to the coupled cluster reference, outperforms current density functional theory models, and thus enables highly accurate simulations of N2O5 in aqueous environments.</p></div></div></div>

scholarly journals A computational study for the reaction mechanism of metal-free cyanomethylation of aryl alkynoates with acetonitrile

RSC Advances ◽  
2021 ◽  
Vol 11 (30) ◽  
pp. 18246-18251
Author(s):  
Selçuk Eşsiz
Keyword(s):  
Density Functional Theory ◽  
Reaction Mechanism ◽  
Density Functional ◽  
Computational Study ◽  
Coupled Cluster ◽  
Functional Theory ◽  
Metal Free ◽  
Coupled Cluster Methods ◽  
High Level ◽  
Cluster Methods

A computational study of metal-free cyanomethylation and cyclization of aryl alkynoates with acetonitrile is carried out employing density functional theory and high-level coupled-cluster methods, such as [CCSD(T)].

Visible-light Photoresponse of Nitrogen-doped TiO2: Excited State Studies Using Time-dependent Density Functional Theory and Equation-of-Motion Coupled Cluster Methods

2010 ◽  
Vol 1263 ◽  
Author(s):  
Niranjan Govind ◽  
Roger Rousseau ◽  
Amity Andersen ◽  
Karol Kowalski
Keyword(s):  
Excited State ◽  
Density Functional ◽  
Equation Of Motion ◽  
Coupled Cluster ◽  
Functional Theory ◽  
Experimental Findings ◽  
High Level ◽  
Cluster Methods ◽  
Compare And Contrast

AbstractTo shed light on the nature of the electronic states at play in N-doped TiO2 nanoparticles, we have performed detailed ground and excited state calculations on pure and N-doped TiO2 rutile using an embedding model. We have validated our model by comparing ground-state embedded results with those obtained from periodic DFT calculations. Our results are consistent with periodic calculations. Using this embedding model we have performed B3LYP based TDDFT calculations of the excited state spectrum. We have also studied the lowest excitations using high-level equation-of-motion coupled cluster (EOMCC) approaches involving all single and inter-band double excitations. We compare and contrast the nature of the excitations in detail for the pure and doped systems using these calculations. Our calculations indicate a lowering of the bandgap and confirm the role of the N3- states on the UV/Vis spectrum of N-doped TiO2 rutile supported by experimental findings.

scholarly journals Density-Functional Tight-Binding for Platinum Clusters and Bulk: Electronic vs Repulsive Parameters

MRS Advances ◽  
2019 ◽  
Vol 4 (33-34) ◽  
pp. 1821-1832
Author(s):  
Ka Hung Lee ◽  
Van Quan Vuong ◽  
Victor Fung ◽  
De-en Jiang ◽  
Stephan Irle
Keyword(s):  
Density Functional ◽  
Tight Binding ◽  
General Purpose ◽  
Training Set ◽  
Functional Theory ◽  
Spin Polarized ◽  
Platinum Clusters ◽  
Electronic Parameters ◽  
Test Sets

ABSTRACTWe present a general purpose Pt-Pt density-functional tight-binding (DFTB) parameter for Pt clusters as well as bulk, using a genetic algorithm (GA) to automatize the parameterization effort. First we quantify the improvement possible by only optimizing the repulsive potential alone, and second we investigate the effect of improving the electronic parameter as well. During both parameterization efforts we employed our own training set and test sets, with one set containing ∼20,000 spin-polarized DFT structures. We analyze the performance of our two DFTB Pt-Pt parameter sets against density functional theory (DFT) as well as an earlier DFTB Pt-Pt parameters. Our study sheds light on the role of both repulsive and electronic parameters with regards to DFTB performance.

scholarly journals Density Functionals with Quantum Chemical Accuracy: From Machine Learning to Molecular Dynamics

2019 ◽  
Author(s):  
Mihail Bogojeski ◽  
Leslie Vogt-Maranto ◽  
Mark E. Tuckerman ◽  
Klaus-Robert Mueller ◽  
Kieron Burke
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Quantum Chemical ◽  
Density Functional ◽  
Md Simulations ◽  
Training Data ◽  
Coupled Cluster ◽  
Density Functionals ◽  
Cluster Accuracy ◽  
Single Water Molecule

<div> <div> <div> <p>Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal/mol with presently-available functionals. <i>Ab initio </i>methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. We create density functionals from coupled-cluster energies, based only on DFT densities, via machine learning. These functionals attain quantum chemical accuracy (errors below 1 kcal/mol). Moreover, density-based ∆-learning (learning only the correction to a standard DFT calculation, ∆-DFT) significantly reduces the amount of training data required. We demonstrate these concepts for a single water molecule, and then illustrate how to include molecular symmetries with ethanol. Finally, we highlight the robustness of ∆-DFT by correcting DFT simulations of resorcinol on the fly to obtain molecular dynamics (MD) trajectories with coupled-cluster accuracy. Thus ∆-DFT opens the door to running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT is quantitatively incorrect. </p> </div> </div> </div>

scholarly journals Quantum chemical accuracy from density functional approximations via machine learning

2019 ◽  
Author(s):  
Mihail Bogojeski ◽  
Leslie Vogt-Maranto ◽  
Mark E. Tuckerman ◽  
Klaus-Robert Mueller ◽  
Kieron Burke
Keyword(s):  
Machine Learning ◽  
Quantum Chemical ◽  
Density Functional ◽  
Md Simulations ◽  
Training Data ◽  
Coupled Cluster ◽  
Functional Theory ◽  
Computational Costs ◽  
Standard Tool ◽  
Cluster Accuracy

<div> <div> <p>Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal/mol with presently-available functionals. <i>Ab initio</i> methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal/mol) on test data. Moreover, density-based ∆-learning (learning only the correction to a standard DFT calculation, termed ∆-DFT) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of ∆-DFT is highlighted by correcting "on the fly" DFT-based molecular dynamics (MD) simulations of resorcinol (C<sub>6</sub>H<sub>4</sub>(OH)<sub>2</sub>) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that ∆-DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.</p> </div> </div>

scholarly journals Quantum Phonon Transport in Nanomaterials: Combining Atomistic with Non-Equilibrium Green’s Function Techniques

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 735 ◽  
Author(s):  
Leonardo Medrano Sandonas ◽  
Rafael Gutierrez ◽  
Alessandro Pecchia ◽  
Alexander Croy ◽  
Gianaurelio Cuniberti
Keyword(s):  
Quantum Transport ◽  
Density Functional ◽  
Tight Binding ◽  
Phonon Transport ◽  
Design Strategies ◽  
Computationally Efficient ◽  
Phonon Thermal Conductivity ◽  
Doping Effects ◽  
Two Dimensional Materials ◽  
Non Equilibrium Green’S Function

A crucial goal for increasing thermal energy harvesting will be to progress towards atomistic design strategies for smart nanodevices and nanomaterials. This requires the combination of computationally efficient atomistic methodologies with quantum transport based approaches. Here, we review our recent work on this problem, by presenting selected applications of the PHONON tool to the description of phonon transport in nanostructured materials. The PHONON tool is a module developed as part of the Density-Functional Tight-Binding (DFTB) software platform. We discuss the anisotropic phonon band structure of selected puckered two-dimensional materials, helical and horizontal doping effects in the phonon thermal conductivity of boron nitride-carbon heteronanotubes, phonon filtering in molecular junctions, and a novel computational methodology to investigate time-dependent phonon transport at the atomistic level. These examples illustrate the versatility of our implementation of phonon transport in combination with density functional-based methods to address specific nanoscale functionalities, thus potentially allowing for designing novel thermal devices.

scholarly journals Density functional tight binding

2014 ◽  
Vol 372 (2011) ◽  
pp. 20120483 ◽  
Author(s):  
Marcus Elstner ◽  
Gotthard Seifert
Keyword(s):  
Density Functional ◽  
Tight Binding ◽  
Taylor Series Expansion ◽  
State Density ◽  
Chemical Hardness ◽  
Computationally Efficient ◽  
Third Order ◽  
Functional Theory ◽  
Basic Principles ◽  
Efficient Representation

This paper reviews the basic principles of the density-functional tight-binding (DFTB) method, which is based on density-functional theory as formulated by Hohenberg, Kohn and Sham (KS-DFT). DFTB consists of a series of models that are derived from a Taylor series expansion of the KS-DFT total energy. In the lowest order (DFTB1), densities and potentials are written as superpositions of atomic densities and potentials. The Kohn–Sham orbitals are then expanded to a set of localized atom-centred functions, which are obtained for spherical symmetric spin-unpolarized neutral atoms self-consistently. The whole Hamilton and overlap matrices contain one- and two-centre contributions only. Therefore, they can be calculated and tabulated in advance as functions of the distance between atomic pairs. The second contributions to DFTB1, the DFT double counting terms, are summarized together with nuclear repulsion energy terms and can be rewritten as the sum of pairwise repulsive terms. The second-order (DFTB2) and third-order (DFTB3) terms in the energy expansion correspond to a self-consistent representation, where the deviation of the ground-state density from the reference density is represented by charge monopoles only. This leads to a computationally efficient representation in terms of atomic charges (Mulliken), chemical hardness (Hubbard) parameters and scaled Coulomb laws. Therefore, no additional adjustable parameters enter the DFTB2 and DFTB3 formalism. The handling of parameters, the efficiency, the performance and extensions of DFTB are briefly discussed.

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