Solving the electronic structure problem for over 100,000 atoms in real-space

Using a real-space high order finite-difference approach, we investigate the electronic structure of large spherical silicon nanoclusters. Within Kohn-Sham density functional theory and using pseudopotentials, we report the self-consistent field convergence of a system with over 100,000 atoms: a Si(107,641)H(9,084) nanocluster with a diameter of 16 nm. Our approach uses Chebyshev-filtered subspace iteration to speed-up the convergence of the eigenspace, and blockwise Hilbert space filling curves to speed-up sparse matrix-vector multiplications, all of which is implemented in the PARSEC code. For the largest system, we utilized 2048 nodes (114,688 processors) on the Frontera machine in the Texas Advanced Computing Center. Our quantitative analysis of the electronic structure shows how it gradually approaches its bulk counterpart as a function of the nanocluster size. The band gap is enlarged due to quantum confinement in nanoclusters, but decreases as the system size increases, as expected. Our work serves as a proof-of-concept for the capacity of the real-space approach in efficiently parallelizing very large calculations using high performance computer platforms, which can straightforwardly be replicated in other systems with more than $10^5$ atoms

Visible Light Responsive Photocatalyst Induces Progressive and Apical-Terminus Preferential Damages on Escherichia coli Surfaces

BACKGROUND: Recent research shows that visible-light responsive photocatalysts have potential usage in antimicrobial applications. However, the dynamic changes in the damage to photocatalyzed bacteria remain unclear. METHODOLOGY/PRINCIPAL FINDINGS: Facilitated by atomic force microscopy, this study analyzes the visible-light driven photocatalyst-mediated damage of Escherichia coli. Results show that antibacterial properties are associated with the appearance of hole-like structures on the bacteria surfaces. Unexpectedly, these hole-like structures were preferentially induced at the apical terminus of rod shaped E. coli cells. Differentiating the damages into various levels and analyzing the percentage of damage to the cells showed that photocatalysis was likely to elicit sequential damages in E. coli cells. The process began with changing the surface properties on bacterial cells, as indicated in surface roughness measurements using atomic force microscopy, and holes then formed at the apical terminus of the cells. The holes were then subsequently enlarged until the cells were totally transformed into a flattened shape. Parallel experiments indicated that photocatalysis-induced bacterial protein leakage is associated with the progression of hole-like damages, further suggesting pore formation. Control experiments using ultraviolet light responsive titanium-dioxide substrates also obtained similar observations, suggesting that this is a general phenomenon of E. coli in response to photocatalysis. CONCLUSION/SIGNIFICANCE: The photocatalysis-mediated localization-preferential damage to E. coli cells reveals the weak points of the bacteria. This might facilitate the investigation of antibacterial mechanism of the photocatalysis

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in Materials Science and Engineering; Address any correspondence to Vikram Gavini ([email protected]) and Danny Perez ([email protected]

A parallel eigensolver for real-space pseudopotential density functional theory calculations

First-principles electronic structure calculations are a popular tool for understanding and predicting properties of materials. Among such methods, the combination of real-space density functional theory and pseudopotentials to solve the Kohn–Sham equation has several advantages. Real-space methods, such as finite differences and finite elements, avoid the global communication needed in fast Fourier transformation and offer better scalability for large calculations on hundreds or thousands of compute nodes. Besides, finite-difference methods with a uniform real-space grid are easy to implement, e.g., the convergence of a Kohn–Sham solution is controlled by a single parameter – the grid spacing. One promising algorithm for solving the Kohn–Sham eigenvalue problem in real space is the Chebyshev-filtered subspace iteration method (CheFSI). Within this algorithm, the charge density is constructed without regard to a solution for individual eigenvalues. However, for large systems CheFSI may suffer from super-linear scaling operations such as orthonormalization and the Rayleigh–Ritz procedure. In the dissertation I will present two improvements in CheFSI to enhance its scalability and accelerate calculation. The first one is a hybrid method that combines a spectrum slicing method and CheFSI. The spectrum slicing method divides a Kohn–Sham eigenvalue problem into subproblems, wherein each subproblem can be solved in parallel using CheFSI. We will show that, by the simulations of confined systems with thousands of atoms, this hybrid method can be faster and possesses better scalability than CheFSI. The second improvement is a grid partitioning method based on space-filling curves. Space-filling curves based grid partitioning improves the efficiency of the sparse matrix–vector multiplication, which is the key component of CheFSI. We will show that, by computations of confined systems with 50,000 atoms or 200,000 electrons, this method effectively reduces the communication overhead and improves the utilization of the vector processing capabilities provided by most modern parallel computers. Along with the improvements, I will also present three applications. One is the study of the evolution of density of states of silicon nanocrystals from small ones to their bulk limit. The simulations can hardly be performed without the improvement in sparse matrix–vector multiplication enhanced by space-filling curves based grid partitioning. The other two applications are the studies of proton transfer in liquid water and the adsorption of water on titanium dioxide surfaces.Chemical Engineerin

Mechanical Properties of Aluminosilicate Nanotubes and Its Application on Desalination

　　本研究第一部分以多尺度模擬方法探討氧化鋁矽奈米管的機械性質。藉由分子力學與材料力學中位能項的對應，我們可以將化學鍵當作樑，進而以結構力學的方法來預測奈米管的機械性質。本研究所使用的方法亦可幫助瞭解鍵結強度與奈米管結構對整體機械強度的貢獻比例，並由von Mises應力分布來找出奈米管結構與整體機械強度的關係。 　　在大多數文獻中對氧化鋁矽奈米管的研究都是基於沒有缺陷的模型，然而實驗上已經發現實際合成的氧化鋁矽奈米管具有許多不同的缺陷。因此我們接續第一部分的研究，發展了一個缺陷氧化鋁矽奈米管模型，希望藉由其來對氧化鋁矽奈米管缺陷、結構穩定性及機械性質之間的關係做一定量探討。我們模型中使用的奈米管缺陷是根據文獻上的實驗結果。對結構穩定性及機械強度的探討是使用多尺度模擬工具，包含密度泛函理論、分子建模及奈米尺度連續體方法。我們的研究也找出了對氧化鋁矽奈米管穩定性及機械強度最有影響的缺陷結構，希望提供其他研究者一個改進材料的方向。 　　本研究第三部分是進行氧化鋁矽奈米管在海水淡化上之效能及應用性的初步評估，包含水的滲透率與阻鹽效果的探討。We investigated the mechanical properties of single-walled aluminosilicate nanotubes (AlSiNTs) using a multiscale computational method and then conducted a comparison with single-walled carbon nanotubes (SWCNTs). By comparing the potential energy estimated from molecular and macroscopic material mechanics, we were able to model the chemical bonds as beam elements for the nanoscale continuum modeling. The proposed approach also enabled the creation of hypothetical nanotubes to elucidate the relative contributions of bond strength and nanotube structural topology to overall nanotube mechanical strength. Our results indicated that it is the structural topology rather than bond strength that dominates the mechanical properties of the nanotubes. Finally, we investigated the relationship between the structural topology and the mechanical properties by analyzing the von Mises stress distribution in the nanotubes. Most existing theoretical studies on the mechanical properties of AlSiNTs are based on defect-free models, despite the fact that experiment results have revealed a variety of defects in AlSiNTs. Herein we developed a method for the modeling of defective AlSiNTs to enable the quantitative investigation of relationships among defect structures, structural stability, and mechanical properties of AlSiNTs. The defect structures dealt with in the proposed models are based on experimental findings. Our assessment of the stability and mechanical strength of nanotubes is based on multiscale computational tools, including density functional theory, molecular modeling, and nanoscale continuum modeling. Our study also identified the defect structure with the most pronounced impact on the stability and mechanical properties of AlSiNTs. In the last part of the study, we investigated the performance and applicability of aluminosilicate nanotube membrane, including water permeability and salt rejection for AlSiNTs with various sizes

Real-space Methods for Electronic Structure Calculations of over 100,000 Atoms

Two factors limit our ability to accurately describe the properties of materials: (1) the ability characterize multiple electron interactions, and (2) the computational tools to solve the resulting equations. With density functional theory (DFT) and the use of pseudopotentials, the electronic structure problem can be effectively solved for many weakly coupled systems. Computational cost of the Kohn–Sham equations is still a problem, frequently restricting the systems of interest to just a few thousand or fewer atoms. Here, we discuss novel methods that let us solve systems that contain more than 100,000 atoms. We concentrate on new computational algorithms based on real-space DFT and pseudopotentials. Our strategy has several benefits. The global communication required for fast Fourier transforms is avoided by real-space formalisms, such as finite differences and finite elements, which also provide superior scalability for big calculations across hundreds or thousands of computer nodes. Furthermore, finite-difference techniques with a uniform real-space grid offer simple implementation; for instance, the grid spacing alone determines how quickly a Kohn-Sham solution converges. Based on a Chebyshev-filtered subspace iteration method (CheFSI), we developed a promising approach for solving the Kohn–Sham equations in real space. We will illustrate two improvements on CheFSI to enhance scalability and accelerate the calculations: (1) a hybrid method that combines a spectrum slicing method and CheFSI, which divides a Kohn–Sham eigenvalue problem into subproblems wherein each subproblem can be solved in parallel using CheFSI; (2) a grid partitioning method based on space-filling curves which improves the efficiency of the sparse matrix–vector multiplication—the key component in CheFSI. We show with computations of confined systems with over 100,000 atoms or 400,000 electrons, that this method effectively reduces the communication overhead and improves the utilization of the vector processing capabilities provided by most modern parallel computers.Texas Advanced Computing Center (TACC

Evaluating Benefits of Eco-Agriculture: The Cases of Farms along Taiwan’s East Coast in Yilan and Hualien

The ecological agriculture (hereinafter referred to as eco-agriculture) concept has grown rapidly in Taiwan in recent years. More and more successful eco-agriculture projects have thus sprouted up in Taiwan, and so a quantitative evaluation model of such projects becomes critically important for improving public understanding of eco-agriculture and for providing a basis for policy analysis. This research thus proposes a quantitative evaluation model for eco-agriculture and analyzes the empirical data collected. We take four farms that practice eco-agriculture in eastern Taiwan for the estimation of direct benefits by surveying farmers about their revenues and costs of crop yields. To evaluate indirect benefits, we employ the Contingent Value Method (CVM) to investigate the willingness-to-pay (WTP) of users and non-users to support eco-agriculture. Results from the direct benefit estimation indicate that eco-agriculture adoption is unlikely to improve the local livelihoods of farming communities. In terms of indirect benefit estimation, eco-agriculture is beneficial to society, but based on our analysis of the direct benefits, these indirect benefits fail to be transformed into profits, showing that eco-agriculture exhibits positive externalities. This constitutes unavoidable challenges for eco-agriculture to be sustainable if these positive externalities cannot be internalized

Bulletin municipal officiel de la ville de Paris. Délibérations des assemblées de la Ville de Paris et du Département de la Seine. Conseil municipal de Paris

20 janvier 19641964/01/20 (N28,A83)- (N29,A83)

5‑Aroylindoles Act as Selective Histone Deacetylase 6 Inhibitors Ameliorating Alzheimer’s Disease Phenotypes

This paper reports the development of a series of 5-aroylindolyl-substituted hydroxamic acids. <i>N</i>-Hydroxy-4-((5-(4-methoxybenzoyl)-1<i>H</i>-indol-1-yl)­methyl)­benzamide (<b>6</b>) has potent inhibitory selectivity against histone deacetylase 6 (HDAC6) with an IC₅₀ value of 3.92 nM. It decreases not only the level of phosphorylation of tau proteins but also the aggregation of tau proteins. Compound <b>6</b> also shows neuroprotective activity by triggering ubiquitination. In animal models, compound <b>6</b> is able to ameliorate the impaired learning and memory, and it crosses the blood–brain barrier after oral administration. Compound <b>6</b> can be developed as a potential treatment for Alzheimer’s disease in the future