Computer simuations for the nano-scale

2007 ◽  
Vol 57 (1) ◽  
pp. 1-176 ◽  
Author(s):  
I. Štich
Keyword(s):  
Quantum Monte Carlo ◽  
Density Functional ◽  
Large Scale ◽  
Hartree Fock ◽  
Monte Carlo Techniques ◽  
Nano Scale ◽  
Scale Modeling ◽  
Starting Point ◽  
Wide Range ◽  
Weak Points

Computer simuations for the nano-scaleA review of methods for computations for the nano-scale is presented. The paper should provide a convenient starting point into computations for the nano-scale as well as a more in depth presentation for those already working in the field of atomic/molecular-scale modeling. The argument is divided in chapters covering the methods for description of the (i) electrons, (ii) ions, and (iii) techniques for efficient solving of the underlying equations. A fairly broad view is taken covering the Hartree-Fock approximation, density functional techniques and quantum Monte-Carlo techniques for electrons. The customary quantum chemistry methods, such as post Hartree-Fock techniques, are only briefly mentioned. Description of both classical and quantum ions is presented. The techniques cover Ehrenfest, Born-Oppenheimer, and Car-Parrinello dynamics. The strong and weak points of both principal and technical nature are analyzed. In the second part we introduce a number of applications to demonstrate the different approximations and techniques introduced in the first part. They cover a wide range of applications such as non-simple liquids, surfaces, molecule-surface interactions, applications in nanotechnology, etc. These more in depth presentations, while certainly not exhaustive, should provide information on technical aspects of the simulations, typical parameters used, and ways of analysis of the huge amounts of data generated in these large-scale supercomputer simulations.

scholarly journals Strange metallicity in the doped Hubbard model

Science ◽  
2019 ◽  
Vol 366 (6468) ◽  
pp. 987-990 ◽  
Author(s):  
Edwin W. Huang ◽  
Ryan Sheppard ◽  
Brian Moritz ◽  
Thomas P. Devereaux
Keyword(s):  
Hubbard Model ◽  
Quantum Monte Carlo ◽  
High Temperature Superconductors ◽  
Linear Temperature Dependence ◽  
Strongly Correlated ◽  
Linear Temperature ◽  
Strongly Correlated Materials ◽  
Starting Point ◽  
Wide Range ◽  
Degenerate Regime

Strange or bad metallic transport, defined by incompatibility with the conventional quasiparticle picture, is a theme common to many strongly correlated materials, including high-temperature superconductors. The Hubbard model represents a minimal starting point for modeling strongly correlated systems. Here we demonstrate strange metallic transport in the doped two-dimensional Hubbard model using determinantal quantum Monte Carlo calculations. Over a wide range of doping, we observe resistivities exceeding the Mott-Ioffe-Regel limit with linear temperature dependence. The temperatures of our calculations extend to as low as 1/40 of the noninteracting bandwidth, placing our findings in the degenerate regime relevant to experimental observations of strange metallicity. Our results provide a foundation for connecting theories of strange metals to models of strongly correlated materials.

scholarly journals The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

2021 ◽  
Author(s):  
Vyshnavi Vennelakanti ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
Spin State ◽  
Density Functional ◽  
Large Scale ◽  
Predictive Power ◽  
Minimum Energy ◽  
Poor Performance ◽  
Closed Shell ◽  
Open Shell ◽  
Methanol Conversion ◽  
Hartree Fock

<p>High-throughput computational catalyst studies are typically carried out using density functional theory (DFT) with a single, approximate exchange-correlation functional. In open shell transition metal complexes (TMCs) that are promising for challenging reactions (e.g., C–H activation), the predictive power of DFT has been challenged, and properties are known to be strongly dependent on the admixture of Hartree-Fock (HF) exchange. We carry out a large-scale study of the effect of HF exchange on the predicted catalytic properties of over 1,200 mid-row (i.e., Cr, Mn, Fe, Co) 3<i>d </i>TMCs for direct methane-to-methanol conversion. Reaction energetic sensitivities across this set depend both on the catalytic rearrangement and ligand chemistry of the catalyst. These differences in sensitivities change both the absolute energetics predicted for a catalyst and its relative performance. Previous observations of the poor performance of global linear free energy relationships (LFERs) hold with both semi-local DFT widely employed in heterogeneous catalysis and hybrid DFT. Narrower metal/oxidation/spin-state specific LFERs perform better and are less sensitive to HF exchange than absolute reaction energetics, except in the case of some intermediate/high-spin states. Importantly, the interplay between spin-state dependent reaction energetics and exchange effects on spin-state ordering means that the choice of DFT functional strongly influences whether the minimum energy pathway is spin-conserved. Despite these caveats, LFERs involving catalysts that can be expected to have closed shell intermediates and low-spin ground states retain significant predictive power.</p>

Design patterns and software techniques for large-scale, open and reproducible data reduction

2021 ◽  
Author(s):  
◽  
Gijs Jan Molenaar
Keyword(s):  
Deep Learning ◽  
Data Reduction ◽  
Large Scale ◽  
Expert Knowledge ◽  
Lessons Learned ◽  
Computational Time ◽  
Generative Adversarial Network ◽  
Software Packages ◽  
Starting Point ◽  
Wide Range

The preparation for the construction of the Square Kilometre Array, and the introduction of its operational precursors, such as LOFAR and MeerKAT, mark the beginning of an exciting era for astronomy. Impressive new data containing valuable science just waiting for discovery is already being generated, and these devices will produce far more data than has ever been collected before. However, with every new data instrument, the data rates grow to unprecedented quantities of data, requiring novel new data-processing tools. In addition, creating science grade data from the raw data still requires significant expert knowledge for processing this data. The software used is often developed by a scientist who lacks proper training in software development skills, resulting in the software not progressing beyond a prototype stage in quality. In the first chapter, we explore various organisational and technical approaches to address these issues by providing a historical overview of the development of radioastronomy pipelines since the inception of the field in the 1940s. In that, the steps required to create a radio image are investigated. We used the lessons-learned to identify patterns in the challenges experienced, and the solutions created to address these over the years. The second chapter describes the mathematical foundations that are essential for radio imaging. In the third chapter, we discuss the production of the KERN Linux distribution, which is a set of software packages containing most radio astronomy software currently in use. Considerable effort was put into making sure that the contained software installs appropriately, all items next to one other on the same system. Where required and possible, bugs and portability fixes were solved and reported with the upstream maintainers. The KERN project also has a website, and issue tracker, where users can report bugs and maintainers can coordinate the packaging effort and new releases. The software packages can be used inside Docker and Singularity containers, enabling the installation of these packages on a wide variety of platforms. In the fourth and fifth chapters, we discuss methods and frameworks for combining the available data reduction tools into recomposable pipelines and introduce the Kliko specification and software. This framework was created to enable end-user astronomers to chain and containerise operations of software in KERN packages. Next, we discuss the Common Workflow Language (CommonWL), a similar but more advanced and mature pipeline framework invented by bio-informatics scientists. CommonWL is supported by a wide range of tools already; among other schedulers, visualisers and editors. Consequently, when a pipeline is made with CommonWL, it can be deployed and manipulated with a wide range of tools. In the final chapter, we attempt something unconventional, applying a generative adversarial network based on deep learning techniques to perform the task of sky brightness reconstruction. Since deep learning methods often require a large number of training samples, we constructed a CommonWL simulation pipeline for creating dirty images and corresponding sky models. This simulated dataset has been made publicly available as the ASTRODECONV2019 dataset. It is shown that this method is useful to perform the restoration and matches the performance of a single clean cycle. In addition, we incorporated domain knowledge by adding the point spread function to the network and by utilising a custom loss function during training. Although it was not possible to improve the cleaning performance of commonly used existing tools, the computational time performance of the approach looks very promising. We suggest that a smaller scope should be the starting point for further studies and optimising of the training of the neural network could produce the desired results.

scholarly journals Benchmarking semiempirical, Hartree–Fock, DFT, and MP2 methods against the ionization energies and electron affinities of short- through long-chain [n]acenes and [n]phenacenes

2016 ◽  
Vol 94 (3) ◽  
pp. 251-258 ◽  
Author(s):  
Sierra Rayne ◽  
Kaya Forest
Keyword(s):  
Density Functional ◽  
Theoretical Data ◽  
Theory Model ◽  
Basis Set ◽  
Electron Affinities ◽  
Ionization Energies ◽  
Hartree Fock ◽  
Wide Range ◽  
High Level ◽  
Long Chain

Vertical and adiabatic ionization energies (IEs) and electron affinities (EAs) were calculated for the n = 1–10 [n]acenes using a wide range of semiempirical, Hartree–Fock, density functional, and second-order Moller–Plesset perturbation theory model chemistries. None of the model chemistries examined was able to accurately predict the IEs or EAs for both short- through long-chain [n]acenes, as well as for extrapolations to the polymeric limit, when compared to available experimental and benchmark theoretical data. Except for 6-31G(d), the choice of the basis set does not affect B3LYP results significantly. Analogous calculations using a suite of eight modern and (or) popular density functionals for the n = 1–10 [n]phenacenes revealed similar problems in estimating the IEs and EAs of these compounds, with the sole exception of the M062X functional for adiabatic IEs and potentially the APFD, B3LYP, and MN12SX functionals for adiabatic EAs. The poor IE/EA prediction performance for the parent [n]acenes and [n]phenacenes may extend to their substituted derivatives and heteroatom-substituted analogs. Consequently, caution should be exercised in the application of non-high-level calculations for estimating the IE/EA of these important classes of materials.

scholarly journals Electronic Properties of Carbon Nanobelts Predicted by Thermally-Assisted-Occupation DFT

Nanomaterials ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2224
Author(s):  
Sonai Seenithurai ◽  
Jeng-Da Chai
Keyword(s):  
Electronic Properties ◽  
Density Functional ◽  
Large Scale ◽  
Von Neumann Entropy ◽  
Electronic Systems ◽  
Functional Theory ◽  
Von Neumann ◽  
Hartree Fock ◽  
Prediction Of Properties ◽  
Occupation Numbers

Accurate prediction of properties of large-scale multi-reference (MR) electronic systems remains difficult for traditional computational methods (e.g., the Hartree–Fock theory and Kohn–Sham density functional theory (DFT)). Recently, thermally-assisted-occupation (TAO)-DFT has been demonstrated to offer reliable description of electronic properties of various large-scale MR electronic systems. Consequently, in this work, TAO-DFT is used to unlock the electronic properties associated with C-Belt[n] (i.e., the carbon nanobelts containing n fused 12-membered carbon rings). Our calculations show that for all the system sizes reported (n = 4–24), C-Belt[n] have singlet ground states. In general, the larger the size of C-Belt[n], the more pronounced the MR character of ground-state C-Belt[n], as evident from the symmetrized von Neumann entropy and the occupation numbers of active TAO-orbitals. Furthermore, the active TAO-orbitals are delocalized along the circumference of C-Belt[n], as evident from the visualization of active TAO-orbitals.

scholarly journals Harnessing the Power of Multi-GPU Acceleration into the Quantum Interaction Computational Kernel Program

2021 ◽  
Author(s):  
Madushanka Manathunga ◽  
Chi Jin ◽  
Vinicius Cruzeiro ◽  
Yipu Miao ◽  
Dawei Mu ◽  
...  
Keyword(s):  
Load Balancing ◽  
Ab Initio ◽  
Density Functional ◽  
Large Scale ◽  
Functional Theory ◽  
Multiple Gpus ◽  
Hartree Fock ◽  
Large Size ◽  
Electron Repulsion Integrals ◽  
Computational Kernel

<div><div><div><p>We report a new multi-GPU capable ab initio Hartree-Fock/density functional theory implementation integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Details on the load balancing algorithms for electron repulsion integrals and exchange correlation quadrature across multiple GPUs are described. Benchmarking studies carried out on up to 4 GPU nodes, each containing 4 NVIDIA V100-SMX2 type GPUs demonstrate that our implementation is capable of achiev- ing excellent load balancing and high parallel efficiency. For representative medium to large size protein/organic molecular sys- tems, the observed efficiencies remained above 86%. The accelerations on NVIDIA A100, P100 and K80 platforms also have real- ized parallel efficiencies higher than 74%, paving the way for large-scale ab initio electronic structure calculations.</p></div></div></div>

scholarly journals Numerical solution of large scale Hartree-Fock-Bogoliubov equations

2021 ◽  
Author(s):  
Lin Lin ◽  
Xiaojie Wu
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Hamiltonian Matrix ◽  
Special Focus ◽  
Matrix Size ◽  
Hartree Fock ◽  
Starting Point ◽  
The Matrix ◽  
The Cost ◽  
Bogoliubov Equations

The Hartree-Fock-Bogoliubov (HFB) theory is the starting point for treating superconducting systems. However, the computational cost for solving large scale HFB equations can be much larger than that of the Hartree-Fock equations, particularly when the Hamiltonian matrix is sparse, and the number of electrons $N$ is relatively small compared to the matrix size $N_{b}$. We first provide a concise and relatively self-contained review of the HFB theory for general finite sized quantum systems, with special focus on the treatment of spin symmetries from a linear algebra perspective. We then demonstrate that the pole expansion and selected inversion (PEXSI) method can be particularly well suited for solving large scale HFB equations. For a Hubbard-type Hamiltonian, the cost of PEXSI is at most $\Or(N_b^2)$ for both gapped and gapless systems, which can be significantly faster than the standard cubic scaling diagonalization methods. We show that PEXSI can solve a two-dimensional Hubbard-Hofstadter model with $N_b$ up to $2.88\times 10^6$, and the wall clock time is less than $100$ s using $17280$ CPU cores. This enables the simulation of physical systems under experimentally realizable magnetic fields, which cannot be otherwise simulated with smaller systems.

scholarly journals Harnessing the Power of Multi-GPU Acceleration into the Quantum Interaction Computational Kernel Program

2021 ◽  
Author(s):  
Madushanka Manathunga ◽  
Chi Jin ◽  
Vinicius Cruzeiro ◽  
Yipu Miao ◽  
Dawei Mu ◽  
...  
Keyword(s):  
Load Balancing ◽  
Ab Initio ◽  
Density Functional ◽  
Large Scale ◽  
Functional Theory ◽  
Multiple Gpus ◽  
Hartree Fock ◽  
Large Size ◽  
Electron Repulsion Integrals ◽  
Computational Kernel

<div><div><div><p>We report a new multi-GPU capable ab initio Hartree-Fock/density functional theory implementation integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Details on the load balancing algorithms for electron repulsion integrals and exchange correlation quadrature across multiple GPUs are described. Benchmarking studies carried out on up to 4 GPU nodes, each containing 4 NVIDIA V100-SMX2 type GPUs demonstrate that our implementation is capable of achiev- ing excellent load balancing and high parallel efficiency. For representative medium to large size protein/organic molecular sys- tems, the observed efficiencies remained above 86%. The accelerations on NVIDIA A100, P100 and K80 platforms also have real- ized parallel efficiencies higher than 74%, paving the way for large-scale ab initio electronic structure calculations.</p></div></div></div>

scholarly journals The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

2021 ◽  
Author(s):  
Vyshnavi Vennelakanti ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
Spin State ◽  
Density Functional ◽  
Large Scale ◽  
Predictive Power ◽  
Minimum Energy ◽  
Poor Performance ◽  
Closed Shell ◽  
Open Shell ◽  
Methanol Conversion ◽  
Hartree Fock

<p>High-throughput computational catalyst studies are typically carried out using density functional theory (DFT) with a single, approximate exchange-correlation functional. In open shell transition metal complexes (TMCs) that are promising for challenging reactions (e.g., C–H activation), the predictive power of DFT has been challenged, and properties are known to be strongly dependent on the admixture of Hartree-Fock (HF) exchange. We carry out a large-scale study of the effect of HF exchange on the predicted catalytic properties of over 1,200 mid-row (i.e., Cr, Mn, Fe, Co) 3<i>d </i>TMCs for direct methane-to-methanol conversion. Reaction energetic sensitivities across this set depend both on the catalytic rearrangement and ligand chemistry of the catalyst. These differences in sensitivities change both the absolute energetics predicted for a catalyst and its relative performance. Previous observations of the poor performance of global linear free energy relationships (LFERs) hold with both semi-local DFT widely employed in heterogeneous catalysis and hybrid DFT. Narrower metal/oxidation/spin-state specific LFERs perform better and are less sensitive to HF exchange than absolute reaction energetics, except in the case of some intermediate/high-spin states. Importantly, the interplay between spin-state dependent reaction energetics and exchange effects on spin-state ordering means that the choice of DFT functional strongly influences whether the minimum energy pathway is spin-conserved. Despite these caveats, LFERs involving catalysts that can be expected to have closed shell intermediates and low-spin ground states retain significant predictive power.</p>

scholarly journals Robust quantum-based interatomic potentials for multiscale modeling in transition metals

2006 ◽  
Vol 21 (3) ◽  
pp. 563-573 ◽  
Author(s):  
John A. Moriarty ◽  
Lorin X. Benedict ◽  
James N. Glosli ◽  
Randolph Q. Hood ◽  
Daniel A. Orlikowski ◽  
...  
Keyword(s):  
Transition Metals ◽  
Multiscale Modeling ◽  
Density Functional ◽  
Large Scale ◽  
Dislocation Core ◽  
Dislocation Dynamics ◽  
Interatomic Potentials ◽  
Dynamic Simulations ◽  
Wide Range ◽  
Dynamics Simulations

First-principles generalized pseudopotential theory (GPT) provides a fundamental basis for transferable multi-ion interatomic potentials in transition metals and alloys within density-functional quantum mechanics. In the central body-centered cubic (bcc) metals, where multi-ion angular forces are important to materials properties, simplified model GPT (MGPT) potentials have been developed based on canonical d bands to allow analytic forms and large-scale atomistic simulations. Robust, advanced-generation MGPT potentials have now been obtained for Ta and Mo and successfully applied to a wide range of structural, thermodynamic, defect, and mechanical properties at both ambient and extreme conditions. Selected applications to multiscale modeling discussed here include dislocation core structure and mobility, atomistically informed dislocation dynamics simulations of plasticity, and thermoelasticity and high-pressure strength modeling. Recent algorithm improvements have provided a more general matrix representation of MGPT beyond canonical bands, allowing improved accuracy and extension to f-electron actinide metals, an order of magnitude increase in computational speed for dynamic simulations, and the development of temperature-dependent potentials.

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