High-Throughput Characterization of Porous Materials Using Graphics Processing Units
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Origin of the magnetic couplings for the weak ferromagnet Li + [TCNE] •- (TCNE = Tetracyanoethylene)
Based upon the 16-K crystal structure, the nearest-neighbor spin couplings (J; H = –2JS a •S b ) for the weak ferromagnet (=canted antiferromagnet) Li + [TCNE] •- (TCNE = tetracyanoethylene) (T c = 21 K), which possesses two interpenetrating diamondoid sublattices with two layers of parallel [TCNE] •- s canted with respect to each other by ~60°, are computed at the B3LYP/aug-cc-pV-TZ-level. Li[TCNE] has computed interlattice, through-space interactions that exceed the intralattice, through-bonding to Li + interactions, that are antiferromagnetic. The interlattice interactions dominate the spin coupling leading to the observed weak ferromagnetic behavior. Finally, the computed J's based upon the 50-K structure have the strongest ferromagnetic intralayer interlattice interactions increase by 2.9 ± 0.6% while the antiferromagnetic interlayer interlattice interaction increases by 23% with respect to the 16K results and are in accord with the observed initial increase and subsequent decrease in the temperature dependence of the canting angle that is a consequence of the these changing opposed interactions arising from the change in structure as a function of temperature.
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MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation
MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics.
00A72↗
Massively parallel implementations of coupled-cluster methods for electron spin resonance spectra. I. Isotropic hyperfine coupling tensors in large radicals
Coupled cluster (CC) methods provide highly accurate predictions of molecular properties, but their high computational cost has precluded their routine application to large systems. Fortunately, recent computational developments in the ACES III program by the Bartlett group [the OED/ERD atomic integral package, the super instruction processor, and the super instruction architecture language] permit overcoming that limitation by providing a framework for massively parallel CC implementations. In that scheme, we are further extending those parallel CC efforts to systematically predict the three main electron spin resonance (ESR) tensors (A-, g-, and D-tensors) to be reported in a series of papers. In this paper inaugurating that series, we report our new ACES III parallel capabilities that calculate isotropic hyperfine coupling constants in 38 neutral, cationic, and anionic radicals that include the {sup 11}B, {sup 17}O, {sup 9}Be, {sup 19}F, {sup 1}H, {sup 13}C, {sup 35}Cl, {sup 33}S,{sup 14}N, {sup 31}P, and {sup 67}Zn nuclei. Present parallel calculations are conducted at the Hartree-Fock (HF), second-order many-body perturbation theory [MBPT(2)], CC singles and doubles (CCSD), and CCSD with perturbative triples [CCSD(T)] levels using Roos augmented double- and triple-zeta atomic natural orbitals basis sets. HF results consistently overestimate isotropic hyperfine coupling constants. However, inclusion of electron correlation effects in the simplest way via MBPT(2) provides significant improvements in the predictions, but not without occasional failures. In contrast, CCSD results are consistently in very good agreement with experimental results. Inclusion of perturbative triples to CCSD via CCSD(T) leads to small improvements in the predictions, which might not compensate for the extra computational effort at a non-iterative N{sup 7}-scaling in CCSD(T). The importance of these accurate computations of isotropic hyperfine coupling constants to elucidate experimental ESR spectra, to interpret spin-density distributions, and to characterize and identify radical species is illustrated with our results from large organic radicals. Those include species relevant for organic chemistry, petroleum industry, and biochemistry, such as the cyclo-hexyl, 1-adamatyl, and Zn-porphycene anion radicals, inter alia.
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Catalytic Deoxydehydration of Carbohydrates and Polyols to Chemicals and Fuels
As the world's fossil fuel resources are being depleted and their costs increase, there is an urgent need to discover and develop new processes for the conversion of renewable, biomass resources into fuels and chemical feedstocks. Research and development in this area have been given high priority by both governmental agencies and industry. To increase the energy content and decrease the boiling points of biomass-derived carbohydrates and polyols to the useful liquid range it is necessary to chemically remove water (dehydrate) and, preferably, oxygen (deoxygenate/reduce). The poly-hydroxylic nature of carbohydrates is attractive for their use as functionalized chemical building blocks, but it presents a daunting challenge for their selective conversion to single product chemicals or fuels. The long term, practical objective of this project is to develop catalytic processes for the deoxydehydration (DODH) of biomass-derived carbohydrates and polyols to produce unsaturated alcohols and hydrocarbons of value as chemical feedstocks and fuels; DODH: polyol + reductant --(LMOx catalyst)--> unsaturate + oxidized reductant + H2O. Limited prior studies have established the viability of the DODH process with expensive phosphine reductants and rhenium-catalysts. Initial studies in the PI's laboratory have now demonstrated: 1) the moderately efficient conversion of glycols to olefins by the economical sulfite salts is catalyzed by MeReO3 and Z+ReO4-; 2) effective phosphine-based catalytic DODH of representative glycols to olefins by cheap LMoO2 complexes; and 3) computational studies (with K. Houk, UCLA) have identified several Mo-, W-, and V-oxo complexes that are likely to catalyze glycol DODH. Seeking practically useful DODH reactions of complex polyols and new understanding of the reactivity of polyoxo-metal species with biomass-oxygenates we will employ a two-pronged approach: 1) investigate experimentally the reactivity, both stoichiometric and catalytic, of polyoxo-complexes and practical reductants with representative polyols to establish structure/reactivity relationships and reaction mechanisms; and b) carry out parallel computational studies of these reactions and their mechanisms- both analytical and predictive. Our prioritized action plan is: (1) to optimize the catalytic efficiency, assess the substrate scope/selectivity, and address key mechanistic aspects of Re-catalyzed, sulfite-driven DODH reactions; (2) use the findings from (1), together with computational predictions, to discover new, effective non-precious metal catalysts for sulfite-driven DODH reactions; and 3) to initiate exploratory studies of CO- and H2-driven DODH. Successful execution of this research project will: 1) provide practical chemical processes for the conversion of biomass into useful chemicals and fuels: 2) bring fundamental new understanding of chemical reactions involving metal-oxo catalysts; and 3) provide educational and technical training of future energy scientists.
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An exploration of the ozone dimer potential energy surface
The (O{sub 3}){sub 2} dimer potential energy surface is thoroughly explored at the ab initio CCSD(T) computational level. Five minima are characterized with binding energies between 0.35 and 2.24 kcal/mol. The most stable may be characterized as slipped parallel, with the two O{sub 3} monomers situated in parallel planes. Partitioning of the interaction energy points to dispersion and exchange as the prime contributors to the stability, with varying contributions from electrostatic energy, which is repulsive in one case. Atoms in Molecules analysis of the wavefunction presents specific O⋯O bonding interactions, whose number is related to the overall stability of each dimer. All internal vibrational frequencies are shifted to the red by dimerization, particularly the antisymmetric stretching mode whose shift is as high as 111 cm{sup −1}. In addition to the five minima, 11 higher-order stationary points are identified.
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Efficient Treatment of Large Active Spaces through Multi-GPU Parallel Implementation of Direct Configuration Interaction
In this study, we have extended our graphical processing unit (GPU)-accelerated direct configuration interaction program to multiple devices, reducing iteration times for configuration spaces of 165 million determinants to only 3 s using NVIDIA P100 GPUs. Similar improvements in the one- and two-particle reduced density matrix formation allow for fast analytical energy gradients and electronic properties. Our parallel algorithm enables the calculation of arbitrarily large configuration spaces (limited only by available system memory), with iteration times of 13 min for an active space of 18 electrons in 18 orbitals (2.4 billion determinants) using six consumer grade NVIDIA 1080Ti GPUs. These advances enable routine molecular dynamics simulations, geometry optimizations, and absorption spectrum calculations for molecules with large configuration spaces, a task that has heretofore required massive computational effort. In this work, we demonstrate the utility of our program by generating the absorption spectrum for diphenyl acetylene at the floating occupation molecular orbital complete active space configuration interaction level of theory. Lastly, several active spaces were investigated to assess the dependence of spectral features on orbital space dimension.
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Influence of longitudinal isotope substitution on the thermal conductivity of carbon nanotubes: Results of nonequilibrium molecular dynamics and local density functional calculations
We report reverse nonequilibrium molecular dynamics calculations of the thermal conductivity of isotope substituted (10,10) carbon nanotubes (CNTs) at 300 K. {sup 12}C and {sup 14}C isotopes both at 50% content were arranged either randomly, in bands running parallel to the main axis of the CNTs or in bands perpendicular to this axis. It is found that the systems with randomly distributed isotopes yield significantly reduced thermal conductivity. In contrast, the systems where the isotopes are organized in patterns parallel to the CNTs axis feature no reduction in thermal conductivity when compared with the pure {sup 14}C system. Moreover, a reduction of approximately 30% is observed in the system with the bands of isotopes running perpendicular to the CNT axis. The computation of phonon dispersion curves in the local density approximation and classical densities of vibrational states reveal that the phonon structure of carbon nanotubes is conserved in the isotope substituted systems with the ordered patterns, yielding high thermal conductivities in spite of the mass heterogeneity. In order to complement our conclusions on the {sup 12}C-{sup 14}C mixtures, we computed the thermal conductivity of systems where the {sup 14}C isotope was turned into pseudo-atoms of 20 and 40 atomic mass units.
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Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations
Spatial stochastic molecular simulations in biology are limited by the intense computation required to track molecules in space either in a discrete time or discrete space framework, which has led to the development of parallel methods that can take advantage of the power of modern supercomputers in recent years. We systematically test suggested components of stochastic reaction-diffusion operator splitting in the literature and discuss their effects on accuracy. We introduce an operator splitting implementation for irregular meshes that enhances accuracy with minimal performance cost. We test a range of models in small-scale MPI simulations from simple diffusion models to realistic biological models and find that multi-dimensional geometry partitioning is an important consideration for optimum performance. We demonstrate performance gains of 1-3 orders of magnitude in the parallel implementation, with peak performance strongly dependent on model specification.
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DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks
Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.
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Activation of Water by Pentavalent Actinide Dioxide Cations: Characteristic Curium Revealed by a Reactivity Turn after Americium
Swapping of an oxygen atom of water with that of a pentavalent actinide dioxide cation, AnO 2 + also called an “actinyl”, requires activation of an An–O bond. It was previously found that such oxo exchange in the gas phase occurs for the first two actinyls, PaO 2 + and UO 2 + , but not the next two, NpO 2 + and PuO 2 + . The An–O bond dissociation energies (BDEs) decrease from PaO 2 + to PuO 2 + , such that the observation of a parallel decrease in the An–O bond reactivity is intriguing. To elucidate oxo exchange, we here extend experimental studies to AmO 2 + , americyl(V), and CmO 2 + , curyl(V), which were produced in remarkable abundance by electrospray ionization of Am 3+ and Cm 3+ solutions. Like other AnO 2 + , americyl(V) and curyl(V) adsorb up to four H 2 O molecules to form tetrahydrates AnO 2 (H 2 O) 4 + with the actinide hexacoordinated by oxygen atoms. It was found that AmO 2 + does not oxo-exchange, whereas CmO 2 + does, establishing a “turn” to increasing the reactivity from americyl to curyl, which validates computational predictions. Because oxo exchange occurs via conversion of an actinyl(V) hydrate, AnO 2 (H 2 O) + , to an actinide(V) hydroxide, AnO(OH) 2 + , it reflects the propensity for actinyl(V) hydrolysis: PaO 2 + hydrolyzes and oxo-exchanges most easily, despite the fact that it has the highest BDE of all AnO 2 + . We note a reexamination of the computational results for actinyl(V) oxo exchange reveals distinctive properties and chemistry of curyl(V) species, particularly CmO(OH) 2 + .
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Critical Factors Driving the High Volumetric Uptake of Methane in Cu 3 (btc) 2
We report a thorough experimental and computational study has been carried out to elucidate the mechanistic reasons for the high volumetric uptake of methane in the metal–organic framework Cu 3 (btc) 2 (btc 3– = 1,3,5-benzenetricarboxylate; HKUST-1). Methane adsorption data measured at several temperatures for Cu 3 (btc) 2 , and its isostructural analogue Cr 3 (btc) 2 , show that there is little difference in volumetric adsorption capacity when the metal center is changed. In situ neutron powder diffraction data obtained for both materials were used to locate four CD 4 adsorption sites that fill sequentially. This data unequivocally shows that primary adsorption sites around, and within, the small octahedral cage in the structure are favored over the exposed Cu 2+ or Cr 2+ cations. These results are supported by an exhaustive parallel computational study, and contradict results recently reported using a time-resolved diffraction structure envelope (TRDSE) method. Moreover, the computational study reveals that strong methane binding at the open metal sites is largely due to methane–methane interactions with adjacent molecules adsorbed at the primary sites instead of an electronic interaction with the metal center. Simulated methane adsorption isotherms for Cu 3 (btc) 2 are shown to exhibit excellent agreement with experimental isotherms, allowing for additional simulations that show that modifications to the metal center, ligand, or even tuning the overall binding enthalpy would not improve the working capacity for methane storage over that measured for Cu 3 (btc) 2 itself.
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Stochastic Vector Techniques in Ground-State Electronic Structure
Herein we review a suite of stochastic vector computational approaches for studying the electronic structure of extended condensed matter systems. These techniques help reduce algorithmic complexity, facilitate efficient parallelization, simplify computational tasks, accelerate calculations, and diminish memory requirements. While their scope is vast, we limit our study to ground-state and finite temperature density functional theory (DFT) and second-order many-body perturbation theory. More advanced topics, such as quasiparticle (charge) and optical (neutral) excitations and higher-order processes, are covered elsewhere. We start by explaining how to use stochastic vectors in computations, characterizing the associated statistical errors. Next, we show how to estimate the electron density in DFT and discuss effective techniques to reduce statistical errors. Finally, we review the use of stochastic vectors for calculating correlation energies within the second-order Møller-Plesset perturbation theory and its finite temperature variational form. Example calculation results are presented and used to demonstrate the efficacy of the methods.
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Controlling colloidal crystals via morphing energy landscapes and reinforcement learning
We report a feedback control method to remove grain boundaries and produce circular shaped colloidal crystals using morphing energy landscapes and reinforcement learning–based policies. We demonstrate this approach in optical microscopy and computer simulation experiments for colloidal particles in ac electric fields. First, we discover how tunable energy landscape shapes and orientations enhance grain boundary motion and crystal morphology relaxation. Next, reinforcement learning is used to develop an optimized control policy to actuate morphing energy landscapes to produce defect-free crystals orders of magnitude faster than natural relaxation times. Morphing energy landscapes mechanistically enable rapid crystal repair via anisotropic stresses to control defect and shape relaxation without melting. This method is scalable for up to at least N = 10 3 particles with mean process times scaling as N 0.5 . Further scalability is possible by controlling parallel local energy landscapes (e.g., periodic landscapes) to generate large-scale global defect-free hierarchical structures.
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Estimating the randomness of quantum circuit ensembles up to 50 qubits
Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. To study this property of random circuits, we develop numerical protocols for estimating the frame potential, the distance between a given ensemble and the exact randomness. Our tensor-network-based algorithm has polynomial complexity for shallow circuits and is high-performing using CPU and GPU parallelism. We study 1. local and parallel random circuits to verify the linear growth in complexity as stated by the Brown–Susskind conjecture, and; 2. hardware-efficient ansätze to shed light on its expressibility and the barren plateau problem in the context of variational algorithms. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.
97 MATHEMATICS AND COMPUTING↗
Uranium (VI) Adsorbate Structures on Portlandite [Ca(OH)2] Type Surfaces Determined by Computational Modelling and X-ray Absorption Spectroscopy
Portlandite [Ca(OH) 2 ] is a potentially dominant solid phase in the high pH fluids expected within the cementitious engineered barriers of Geological Disposal Facilities (GDF). This study combined X-ray Absorption Spectroscopy with computational modelling in order to provide atomic-scale data which improves our understanding of how a critically important radionuclide (U) will be adsorbed onto this phase under conditions relevant to a GDF environment. Such data are fundamental for predicting radionuclide mass transfer. Surface coordination chemistry and speciation of uranium with portlandite [Ca(OH) 2 ] under alkaline groundwater conditions (ca. pH 12) were determined by both in situ and ex situ grazing incidence extended X-ray absorption fine structure analysis (EXAFS) and by computational modelling at the atomic level. Free energies of sorption of aqueous uranyl hydroxides, [UO 2 (OH) n ] 2–n (n = 0–5) with the (001), (100) and (203) or (101) surfaces of portlandite are predicted from the potential of mean force using classical molecular umbrella sampling simulation methods and the structural interactions are further explored using fully periodic density functional theory computations. Although uranyl is predicted to only weakly adsorb to the (001) and (100) clean surfaces, there should be significantly stronger interactions with the (203/101) surface or at hydroxyl vacancies, both prevalent under groundwater conditions. The uranyl surface complex is typically found to include four equatorially coordinated hydroxyl ligands, forming an inner-sphere sorbate by direct interaction of a uranyl oxygen with surface calcium ions in both the (001) and (203/101) cases. In contrast, on the (100) surface, uranyl is sorbed with its axis more parallel to the surface plane. The EXAFS data are largely consistent with a surface structural layer or film similar to calcium uranate, but also show distinct uranyl characteristics, with the uranyl ion exhibiting the classic dioxygenyl oxygens at 1.8 Å and between four and five equatorial oxygen atoms at distances between 2.28 and 2.35 Å from the central U absorber. These experimental data are wholly consistent with the adsorbate configuration predicted by the computational models. These findings suggest that, under the strongly alkaline conditions of a cementitious backfill engineered barrier, there would be significant uptake of uranyl by portlandite to inhibit the mobility of U(VI) from the near field of a geological disposal facility.
36 MATERIALS SCIENCE↗
Analytic energy gradient of excited electronic state within TDDFT/MMpol framework: Benchmark tests and parallel implementation
The time-dependent density functional theory (TDDFT) has become the most popular method to calculate the electronic excitation energies, describe the excited-state properties, and perform the excited-state geometric optimization of medium and large-size molecules due to the implementation of analytic excited-state energy gradient and Hessian in many electronic structure software packages. To describe the molecules in condensed phase, one usually adopts the computationally efficient hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) models. Here, we extend our previous work on the energy gradient of TDDFT/MM excited state to account for the mutual polarization effects between QM and MM regions, which is believed to hold a crucial position in the potential energy surface of molecular systems when the photoexcitation-induced charge rearrangement in the QM region is drastic. The implementation of a simple polarizable TDDFT/MM (TDDFT/MMpol) model in Q-Chem/CHARMM interface with both the linear response and the state-specific features has been realized. Several benchmark tests and preliminary applications are exhibited to confirm our implementation and assess the effects of different treatment of environmental polarization on the excited-state properties, and the efficiency of parallel implementation is demonstrated as well.
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