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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Gas-phase ion-molecule interactions in a collision reaction cell with triple quadrupole-inductively coupled plasma mass spectrometry: Investigations with N 2 O as the reaction gas

Nitrous oxide (N 2 O) was used as a reaction gas to investigate the gas phase ion-molecule interactions using the Agilent 8900 QQQ-ICP-MS. A multi-element standard containing 45 elements with masses ranging from 9 to 208 u was measured in the presence and absence of N 2 O. The main product ion species observed were oxides and nitrides. Comparison of the N 2 O reaction results with similar measurements conducted with O 2 revealed that N 2 O was more effective at forming oxides in general: the elements Cd and Pb were shown to produce oxides with N 2 O where the reaction did not occur with O 2 . Nitrous oxide was also shown to produce a significant amount of nitride species in a few cases. The general reactivity was shown to be consistent with density functional theory (DFT)-predicted reaction enthalpies, such that all predicted exothermic reactions produced product ions at levels at least 1% of the unreacted ion. Our results show that reaction enthalpy is a reasonable predictor of reactivity with N2O on the timescales of the interactions in non-thermal ICP-MS/MS systems. Our work demonstrates the utility of two relatively new platforms (commercial elemental ICP-MS/MS and EMSL Arrows interface to the NWChem program suite), which allows for the study of a large number of elements within a short period. While DFT with the basis sets utilized here is not the most accurate computational method, it is also not computationally expensive and is shown to be suitable for predicting gas phase reactivity in the QQQ-ICP-MS for the majority of ions studied. Here, the ease and rapidity of data collection and DFT calculations has the potential to be very impactful for the identification of targeted reaction chemistries to be leveraged for analytical method development, such as for the inline separation of isobaric interferences from analytes of interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

SparcleQC: Automated Input File Creation for QM/MM Studies of Protein:Ligand Complexes

SparcleQC is a Python package that, given a protein:ligand complex in the Protein Data Bank (PDB) file format, can create quantum mechanics/molecular mechanics (QM/MM)-like input files for the electronic structure theory packages PSI4, QChem, and NWChem. The resulting input files include quantum mechanical representations of the ligand and a small section of the protein, surrounded by point charges that represent the rest of the protein. Creation of these QM/MM input files includes cutting and capping the QM subregion, obtaining point charges for the protein, and adjusting charges at the QM/MM boundary; and each of these tasks are automated by the software. In this article, we describe the details of SparcleQC’s procedure, show examples of the Python API, and explain additional features that are helpful in protein:ligand interaction studies. Finally, we show that SparcleQC enables automated preparation of input files for QM/MM calculations, which can return can return accurate interaction energies in minutes, while a fully quantum mechanical computation on the protein:ligand complex could take days, if it is even possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

WFOT: A Wave Function Overlap Tool between Single- and Multi-Reference Electronic Structure Methods for Spectroscopy Simulation

We report the development of a novel diagnostic tool, named wave function overlap tool (WFOT), designed to evaluate the overlap between wave functions computed at single-reference [i.e., time-dependent density functional theory or configuration interaction singles (CIS)] and multireference (i.e., CASSCF/CASPT2) electronic structure levels of theory. It relies on truncating the single- and multireference WFs to CIS-like expansions spanning the same configurational space and maximizing the molecular orbital overlap by means of a unitary transformation. To demonstrate the functionality of the tool, we calculate the transient spectrum of acetylacetone by evaluating excited state absorption signals with multireference quality on top of single-reference on-the-fly dynamics simulations. Semiautomatic spectra generation is facilitated by interfacing the tool with the COBRAMM package, which also allows one to use WFOT with several quantum chemistry codes such as Gaussian, NWChem, and OpenMolcas. Finally, other exciting possibilities for the utilization of the code beyond the simulation of transient absorption spectroscopy are eventually discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Infrared and Raman Spectroscopy from Ab Initio Molecular Dynamics and Static Normal Mode Analysis: The C–H Region of DMSO as a Case Study

Carbon-hydrogen (C-H) vibration modes serve as key probes in the chemical identification of hydrocarbons and in vibrational sum-frequency generation (SFG) spectroscopy of hydrocarbons at the liquid/gas interface. Their assignments pose a challenge from a theoretical viewpoint. Here in this work, we present a detailed study of the C-H stretching region of dimethyl sulfoxide (DMSO) using a new Gaussian basis set- based ab initio molecular dynamics (AIMD) module that we have implemented in the NWChem computational chemistry program. By combining AIMD simulations and static normal mode analysis, we interpret experimental infrared and Raman spectra and explore the role of anharmonic effects in this system. Our anharmonic normal mode analysis of the in-phase and out-of-phase symmetric C-H stretching modes challenges the previous experimental assignment of the shoulder in the symmetric C-H stretching peak as an overtone or Fermi resonance. In addition, our AIMD simulations also show significant broadening of the in-phase symmetric C-H stretching resonance, which suggests that the experimentally observed shoulder is due to thermal broadening of the symmetric stretching resonance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Coupled Cluster Theory on Graphics Processing Units I. The Coupled Cluster Doubles Method

The coupled cluster (CC) ansatz is generally recognized as providing one of the best wave function-based descriptions of electronic correlation in small- and medium-sized molecules. The fact that the CC equations with double excitations (CCD) may be expressed as a handful of dense matrix-matrix multiplications makes it an ideal method to be ported to graphics processing units (GPUs). Here we present our implementation of the spin-free CCD equations in which the entire iterative procedure is evaluated on the GPU. The GPU-accelerated algorithm readily achieves a factor of 4-5 speedup relative to the multithreaded CPU algorithm on same-generation hardware. The GPU-accelerated algorithm is approximately 8-12 times faster than Molpro, 17-22 times faster than NWChem, and 21-29 times faster than GAMESS for each CC iteration. Single-precision GPU-accelerated computations are also performed, leading to an additional doubling of performance. Single-precision errors in the energy are typically on the order of 10 -6 hartrees and can be improved by about an order of magnitude by performing one additional iteration in double precision.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Modeling fast electron dynamics with real-time time-dependent density functional theory: application to small molecules and chromophores

The response of matter to external fields forms the basis for a vast wealth of fundamental physical processes ranging from light harvesting to nanoscale electron transport. Accurately modeling ultrafast electron dynamics in excited systems thus o_ers unparalleled insight, but requires an inherently non-linear time-resolved approach. To this end, an e_cient and massively parallel real-time real-space time-dependent density functional theory (RT-TDDFT) implementation in NWChem is presented. The implementation is first validated against linearresponse TDDFT and experimental results for a series of molecules subjected to small electric field perturbations. Second, non-linear excitation of green fluorescent protein is studied, which shows a blue-shift in the spectrum with increasing perturbation, as well as a saturation in absorption. Next, the charge dynamics of optically excited zinc porphyrin is presented in real-time and real-space, with relevance to charge injection in photovoltaic devices. Finally, intermolecular excitation in an adenine-thymine base pair is studied using the BNL range separated functional [Baer, R.; Neuhauser, D. Phys. Rev. Lett. 2005, 94, 043002], demonstrating the utility of a real-time approach in capturing charge transfer processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Scalar relativistic computations of nuclear magnetic shielding and g-shifts with the zeroth-order regular approximation and range-separated hybrid density functionals

Density functional theory (DFT) calculations of NMR chemical shifts and molecular g-tensors with Gaussian-type orbitals are implemented via second-order energy derivatives within the scalar relativistic zeroth order regular approximation (ZORA) framework. Nonhybrid functionals, standard (global) hybrids, and range-separated (Coulomb-attenuated, long-range corrected) hybrid functionals are tested. Origin invariance of the results is ensured by use of gauge-including atomic orbital (GIAO) basis functions. The new implementation in the NWChem quantum chemistry package is verified by calculations of nuclear shielding constants for the heavy atoms in HX (X=F, Cl, Br, I, At) and H2X (X = O, S, Se, Te, Po), and Te chemical shifts in a number of tellurium compounds. The basis set and functional dependence of g-shifts is investigated for 14 radicals with light and heavy atoms. The problem of accurately predicting F NMR shielding in UF6-nCln, n = 1 to 6, is revisited. The results are sensitive to approximations in the density functionals, indicating a delicate balance of DFT self-interaction vs. correlation. For the uranium halides, the results with the range-separated functionals are mixed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Scalar Relativistic Computations and Localized Orbital Analyses of Nuclear Hyperfine Coupling and Paramagnetic NMR Chemical Shifts

A method is reported by which calculated hyperfine coupling constants (HFCCs) and paramagnetic NMR (pNMR) chemical shifts can be analyzed in a chemically intuitive way by decomposition into contributions from localized molecular orbitals (LMOs). A new module for density functional calculations with nonhybrid functionals, global hybrids, and range-separated hybrids, utilizing the two-component relativistic zeroth-order regular approximation (ZORA), has been implemented in the parallel open-source NWChem quantum chemistry package. Benchmark results are reported for a test set of few-atom molecules with light and heavy elements. Finite nucleus effects on ¹⁹⁹Hg HFCCs are shown to be on the order of -11 to -15%. A proof of concept for the LMO analysis is provided for the metal and fluorine HFCCs of TiF₃ and NpF₆. Calculated pNMR chemical shifts are reported for the 2-methylphenyl-t-butylnitroxide radical and for five cyclopentadienyl (Cp) sandwich complexes with 3d metals. Nickelocene and vanadocene carbon pNMR shifts are analyzed in detail, demonstrating that the large carbon pNMR shifts calculated as +1540 for Ni (exptl.: +1514) and -443 for V (exptl.: -510) are caused by different spin-polarization mechanisms. For Ni, Cp to Ni π back-donation dominates the result, whereas for vanadocene, V to Cp σ donation with relaxation of the carbon 1s shells can be identified as the dominant mechanism.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2012

Two-Component Relativistic Calculations of Electric-Field Gradients Using Exact Decoupling Methods: Spin–orbit and Picture-Change Effects

Electric field gradients (EFGs) for heavy nuclei in diatomic molecules (hydrogen halides and group-13 iodides), uranyl (UO 2 2+ ), and a uranyl carbonate complex, were computed with different formally exactly (or nearly exactly) decoupled two-component relativistic Hamiltonians: the “exact two-component” (X2C) method, the Barysz–Sadlej–Snijders (BSS) approach, and up to 35th order in the Douglas–Kroll–Hess (DKH) expansion, utilizing a new implementation in the open-source NWChem quantum chemistry package. Results from two-component Hartree–Fock and density functional theory (DFT) calculations at the scalar relativistic approximation as well as including spin–orbit coupling are reported. Picture-change corrected EFGs obtained with X2C, BSS, and high-order DKH are shown to be numerically equivalent. Effects from spin–orbit coupling on the EFGs tend to be moderate but should not be excluded for reliable predictions. Picture-change effects on the EFG of a heavy atom can be as large as the correct picture-change corrected value, as is shown for the uranium EFG in the uranyl systems. The results demonstrate the necessity of simultaneously including spin–orbit and picture-change effects in calculations of heavy atom EFGs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations

A new large-scale parallel multiconfigurational self-consistent field (MCSCF) implementation in the open-source NWChem computational chemistry code is presented. The generalized active space approach is used to partition large configuration interaction (CI) vectors and generate a sufficient number of batches that can be distributed to the available cores. Massively parallel CI calculations with large active spaces can be performed. The new parallel MCSCF implementation is tested for the chromium trimer and for an active space of 20 electrons in 20 orbitals, which can now routinely be performed. Unprecedented CI calculations with an active space of 22 electrons in 22 orbitals for the pentacene systems were performed and a single CI iteration calculation with an active space of 24 electrons in 24 orbitals for the chromium tetramer was possible. In conclusion, the chromium tetramer corresponds to a CI expansion of one trillion Slater determinants (914 058 513 424) and is the largest conventional CI calculation attempted up to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

We report that community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2012

Load Balancing of Dynamical Nucleation Theory Monte Carlo Simulations through Resource Sharing Barriers

The dynamical nucleation theory Monte Carlo (DNTMC) application from the NWChem computational chemistry suite utilizes a Markov chain Monte Carlo, two-level parallel structure, with periodic synchronization points that assemble the results of independent finer-grained calculations. Like many such applications, the existing code employs a static partitioning of processes into groups and assigns each group a piece of the finer-grained parallel calculation. A significant cause of performance degradation is load imbalance among groups since the time requirements of the inner-parallel calculation varies widely with the input problem and as a result of the Monte Carlo simulation. We present a novel approach to load balancing such calculations with minimal changes to the application. We introduce the concept of a resource sharing barrier (RSB) – a barrier that allows process groups waiting on other processes’ work to actively contribute to their completion. The RSB load balancing technique is applied to the production DNTMC application code, resulting in a small code change of 200 lines and a reduction in execution time of up to 37%.

Arafat, Md H.↗
DOE OSTI2016

Fault Modeling of Extreme Scale Applications Using Machine Learning

Faults are commonplace in large scale systems. These systems experience a variety of faults such as transient, permanent and intermittent. Multi-bit faults are typically not corrected by the hardware resulting in an error. Here, this paper attempts to answer an important question: Given a multi-bit fault in main memory, will it result in an application error — and hence a recovery algorithm should be invoked — or can it be safely ignored? We propose an application fault modeling methodology to answer this question. Given a fault signature (a set of attributes comprising of system and application state), we use machine learning to create a model which predicts whether a multibit permanent/transient main memory fault will likely result in error. We present the design elements such as the fault injection methodology for covering important data structures, the application and system attributes which should be used for learning the model, the supervised learning algorithms (and potentially ensembles), and important metrics. Lastly, we use three applications — NWChem, LULESH and SVM — as examples for demonstrating the effectiveness of the proposed fault modeling methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Towards Highly Scalable Ab Initio Molecular Dynamics (AIMD) Simulations on the Intel Knights Landing Manycore Processor

The Ab Initio Molecular Dynamics (AIMD) method allows scientists to treat the dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. This extremely important method has tremendous computational requirements, because the electronic Schr¨odinger equation, approximated using Kohn-Sham Density Functional Theory (DFT), is solved at every time step. With the advent of manycore architectures, application developers have a significant amount of processing power within each compute node that can only be exploited through massive parallelism. A compute intensive application such as AIMD forms a good candidate to leverage this processing power. In this paper, we focus on adding thread level parallelism to the plane wave DFT methodology implemented in NWChem. Through a careful optimization of tall-skinny matrix products, which are at the heart of the Lagrange multiplier and nonlocal pseudopotential kernels, as well as 3D FFTs, our OpenMP implementation delivers excellent strong scaling on the latest Intel Knights Landing (KNL) processor. We assess the efficiency of our Lagrange multiplier kernels by building a Roofline model of the platform, and verify that our implementation is close to the roofline for various problem sizes. Finally, we present strong scaling results on the complete AIMD simulation for a 64 water molecules test case, that scales up to all 68 cores of the Knights Landing processor.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2013

Building Scalable PGAS Communication Subsystem on Blue Gene/Q

This paper presents a design of scalable Partitioned Global Address Space (PGAS) communication subsystems on recently proposed Blue Gene/Q architecture. The proposed design provides an in-depth modeling of communication infrastructure using Parallel Active Messaging Interface (PAMI). The communication infrastructure is used to design time-space efficient communication protocols for frequently used data-types (contiguous, uniformly non-contiguous) using Remote Direct Memory Access (RDMA) get/put primitives. The proposed design accelerates load balance counters by using asynchronous threads, which are required due to the missing network hardware support for Atomic Memory Operations (AMOs). Under the proposed design, the synchronization traffic is reduced by tracking conflicting memory accesses in distributed space with slightly increment in space complexity. An evaluation with simple communication benchmarks show a adjacent node get latency of 2.89$\mu$s and peak bandwidth of 1775 MB/s resulting in $\approx$ 99\% communication efficiency.The evaluation shows a reduction in the execution time by up to 30\% for NWChem self consistent field calculation on 4096 processes using the proposed asynchronous thread based design.

Vishnu, Abhinav↗
DOE OSTI2014

Efficient Implementation of Many-body Quantum Chemical Methods on the Intel Xeon Phi Coprocessor

This paper presents the implementation and performance of the highly accurate CCSD(T) quantum chemistry method on the Intel Xeon Phi coprocessor within the context of the NWChem computational chemistry package. The widespread use of highly correlated methods in electronic structure calculations is contingent upon the interplay between advances in theory and the possibility of utilizing the ever-growing computer power of emerging heterogeneous architectures. We discuss the design decisions of our implementation as well as the optimizations applied to the compute kernels and data transfers between host and coprocessor. We show the feasibility of adopting the Intel Many Integrated Core Architecture and the Intel Xeon Phi coprocessor for developing efficient computational chemistry modeling tools. Remarkable scalability is demonstrated by benchmarks. Our solution scales up to a total of 62560 cores with the concurrent utilization of Intel Xeon processors and Intel Xeon Phi coprocessors.

Environmental Molecular Sciences Laboratory↗