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At least 181 records · Page 10

Ensemble Sampling vs. Time Sampling in Molecular Dynamics Simulations of Thermal Conductivity

In this report we compare time sampling and ensemble averaging as two different methods available for phase space sampling. For the comparison, we calculate thermal conductivities of solid argon and silicon structures, using equilibrium molecular dynamics. We introduce two different schemes for the ensemble averaging approach, and show that both can reduce the total simulation time as compared to time averaging. It is also found that velocity rescaling is an efficient mechanism for phase space exploration. Although our methodology is tested using classical molecular dynamics, the ensemble generation approaches may find their greatest utility in computationally expensive simulations such as first principles molecular dynamics. For such simulations, where each time step is costly, time sampling can require long simulation times because each time step must be evaluated sequentially and therefore phase space averaging is achieved through sequential operations. On the other hand, with ensemble averaging, phase space sampling can be achieved through parallel operations, since each ensemble is independent. For this reason, particularly when using massively parallel architectures, ensemble sampling can result in much shorter simulation times and exhibits similar overall computational effort.

74 ATOMIC AND MOLECULAR PHYSICS↗

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↗

CAST: Contraction Algorithm for Symmetric Tensors

Tensor contractions represent the most compute-intensive core kernels in ab initio computational quantum chemistry and nuclear physics. Symmetries in these tensor contractions makes them difficult to load balance and scale to large distributed systems. In this paper, we develop an efficient and scalable algorithm to contract symmetric tensors. We introduce a novel approach that avoids data redistribution in contracting symmetric tensors while also avoiding redundant storage and maintaining load balance. We present experimental results on two parallel supercomputers for several symmetric contractions that appear in the CCSD quantum chemistry method. We also present a novel approach to tensor redistribution that can take advantage of parallel hyperplanes when the initial distribution has replicated dimensions, and use collective broadcast when the final distribution has replicated dimensions, making the algorithm very efficient.

distributed memory↗

AENET–LAMMPS and AENET–TINKER : Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A meshless stochastic method for Poisson–Nernst–Planck equations

A plethora of biological, physical, and chemical phenomena involve transport of charged particles (ions). Its continuum-scale description relies on the Poisson–Nernst–Planck (PNP) system, which encapsulates the conservation of mass and charge. The numerical solution of these coupled partial differential equations is challenging and suffers from both the curse of dimensionality and difficulty in efficiently parallelizing. We present a novel particle-based framework to solve the full PNP system by simulating a drift–diffusion process with time- and space-varying drift. We leverage Green’s functions, kernel-independent fast multipole methods, and kernel density estimation to solve the PNP system in a meshless manner, capable of handling discontinuous initial states. The method is embarrassingly parallel, and the computational cost scales linearly with the number of particles and dimension. We use a series of numerical experiments to demonstrate both the method’s convergence with respect to the number of particles and computational cost vis-à-vis a traditional partial differential equation solver.

Chemistry↗

Effect of Surface Nonequilibrium Thermochemistry in Simulation of Carbon Based Ablators

This study demonstrates that coupling of a material thermal response code and a flow solver using finite-rate gas/surface interaction model provides time-accurate solutions for multidimensional ablation of carbon based charring ablators. The material thermal response code used in this study is the Two-dimensional Implicit Thermal Response and Ablation Program (TITAN), which predicts charring material thermal response and shape change on hypersonic space vehicles. Its governing equations include total energy balance, pyrolysis gas momentum conservation, and a three-component decomposition model. The flow code solves the reacting Navier-Stokes equations using Data Parallel Line Relaxation (DPLR) method. Loose coupling between material response and flow codes is performed by solving the surface mass balance in DPLR and the surface energy balance in TITAN. Thus, the material surface recession is predicted by finite-rate gas/surface interaction boundary conditions implemented in DPLR, and the surface temperature and pyrolysis gas injection rate are computed in TITAN. Two sets of gas/surface interaction chemistry between air and carbon surface developed by Park and Zhluktov, respectively, are studied. Coupled fluid-material response analyses of stagnation tests conducted in NASA Ames Research Center arc-jet facilities are considered. The ablating material used in these arc-jet tests was a Phenolic Impregnated Carbon Ablator (PICA). Computational predictions of in-depth material thermal response and surface recession are compared with the experimental measurements for stagnation cold wall heat flux ranging from 107 to 1100 Watts per square centimeter.

Chen, Yih-Kang↗

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adsorption, Orientation, and Speciation of Amino Acids at Air–Aqueous Interfaces for the Direct Air Capture of CO 2

Amino acids make up a promising family of molecules capable of direct air capture (DAC) of CO 2 from the atmosphere. Under alkaline conditions, CO 2 reacts with the anionic form of an amino acid to produce carbamates and deactivated zwitterionic amino acids. The presence of the various species of amino acids and reactive intermediates can have a significant effect on DAC chemistry, the role of which is poorly understood. In this study, all-atom molecular dynamics (MD) based computational simulations and vibrational sum frequency generation (vSFG) spectroscopy studies were conducted to understand the role of competitive interactions at the air–aqueous interface in the context of DAC. We find that the presence of potassium bicarbonate ions, in combination with the anionic and zwitterionic forms of amino acids, induces concentration and charge gradients at the interface, generating a layered molecular arrangement that changes under pre- and post-DAC conditions. In parallel, an enhancement in the surface activity of both anionic and zwitterionic forms of amino acids is observed, which is attributed to enhanced interfacial stability and favorable intermolecular interactions between the adsorbed amino acids in their anionic and zwitterionic forms. The collective influence of these competitive interactions, along with the resulting interfacial heterogeneity, may in turn affect subsequent capture reactions and associated rates. Finally, these effects underscore the need to consider dynamic changes in interfacial chemical makeup to enhance DAC efficiency and to develop successful negative emission and storage technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of Passive HC/NOx Trap Catalysts for Low Temperature Gasoline Applications

This project aimed to develop fundamental understanding of the chemistry of NO adsorption and reaction in Pd/zeolites so as to facilitate the rational design of passive NOx adsorber catalysts. The approach adopted combined both experimental and computational methods, which together allow a deeper understanding of the governing chemistry than the use of either method alone. The workflow began with Pd/H-CHA and Pd/H-BEA catalyst synthesis and characterization, in which the Si/Al ratio and Al siting were systematically varied. This was followed by catalyst evaluation using temperature-programed adsorption/desorption methods, as well as in situ spectroscopic measurements to probe the chemistry of NO adsorption. In parallel, the adsorption of NO and other relevant species (H 2 O, CO, HCs) was studied by means of quantum chemical calculations in order to rationalize the experimental data and provide additional insights. Catalyst aging studies were also performed with the aim of elucidating the mechanism of catalyst degradation. Finally, the insights gained in this project were applied to the preparation of an optimized HC/NOx adsorber catalyst, the performance of which was studied using exhaust gas from an engine dynamometer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reconstructing three-dimensional protein crystal intensities from sparse unoriented two-axis X-ray diffraction patterns

Recently, there has been a growing interest in adapting serial microcrystallography (SMX) experiments to existing storage ring (SR) sources. For very small crystals, however, radiation damage occurs before sufficient numbers of photons are diffracted to determine the orientation of the crystal. The challenge is to merge data from a large number of such `sparse' frames in order to measure the full reciprocal space intensity. To simulate sparse frames, a dataset was collected from a large lysozyme crystal illuminated by a dim X-ray source. The crystal was continuously rotated about two orthogonal axes to sample a subset of the rotation space. With the EMC algorithm [expand–maximize–compress; Loh & Elser (2009). Phys. Rev. E , 80 , 026705], it is shown that the diffracted intensity of the crystal can still be reconstructed even without knowledge of the orientation of the crystal in any sparse frame. Moreover, parallel computation implementations were designed to considerably improve the time and memory scaling of the algorithm. The results show that EMC-based SMX experiments should be feasible at SR sources.

Chemistry↗

Effect of Non-Equilibrium Surface Thermochemistry in Simulation of Carbon Based Ablators

This study demonstrates that coupling of a material thermal response code and a flow solver using non-equilibrium gas/surface interaction model provides time-accurate solutions for the multidimensional ablation of carbon based charring ablators. The material thermal response code used in this study is the Two-dimensional Implicit Thermal-response and AblatioN Program (TITAN), which predicts charring material thermal response and shape change on hypersonic space vehicles. Its governing equations include total energy balance, pyrolysis gas mass conservation, and a three-component decomposition model. The flow code solves the reacting Navier-Stokes equations using Data Parallel Line Relaxation (DPLR) method. Loose coupling between the material response and flow codes is performed by solving the surface mass balance in DPLR and the surface energy balance in TITAN. Thus, the material surface recession is predicted by finite-rate gas/surface interaction boundary conditions implemented in DPLR, and the surface temperature and pyrolysis gas injection rate are computed in TITAN. Two sets of nonequilibrium gas/surface interaction chemistry between air and the carbon surface developed by Park and Zhluktov, respectively, are studied. Coupled fluid-material response analyses of stagnation tests conducted in NASA Ames Research Center arc-jet facilities are considered. The ablating material used in these arc-jet tests was Phenolic Impregnated Carbon Ablator (PICA). Computational predictions of in-depth material thermal response and surface recession are compared with the experimental measurements for stagnation cold wall heat flux ranging from 107 to 1100 Watts per square centimeter.

Chen, Yih-Kanq↗

Collective mode Brownian dynamics: A method for fast relaxation of statistical ensembles

Sampling equilibrium configurations of correlated systems of particles with long relaxation times (e.g., polymeric solutions) using conventional molecular dynamics and Monte Carlo methods can be challenging. This is especially true for systems with complicated, extended bond network topologies and other interactions that make the use and design of specialized relaxation protocols infeasible. We introduce a method based on Brownian dynamics simulations that can reduce the computational time it takes to reach equilibrium and draw decorrelated samples. Importantly, the method is completely agnostic to the particle configuration and the specifics of interparticle forces. In particular, we develop a mobility matrix that excites non-local, collective motion of N particles and can be computed efficiently in $\mathcal{O}(N)$ time. Particle motion in this scheme is computed by integrating the overdamped Langevin equation with an Euler–Maruyama scheme, in which Brownian displacements are drawn efficiently using a low-rank representation of the mobility matrix in position and wave space. We demonstrate the efficacy of the method with various examples from the realm of soft condensed matter and release a massively parallel implementation of the code as a plugin for the open-source package HOOMD-blue [J. A. Anderson et al., J. Comput. Phys. 227, 5342 (2008) and J. Glaser et al., Comput. Phys. Commun. 192, 97 (2015)] which runs on graphics processing units

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Research and Educational Opportunities with NASA GSFC

The research carried out in the Earth sciences at NASA's Goddard Space Flight Center will be the focus of the presentation. These research interests span the full breath of disciplines in Earth System Science. Branches and research groups focus on areas as diverse as planetary geomagnetics and atmospheric chemistry. There are 4 major laboratories in the directorate and 2 organizations that focus on scientific information technology. These laboratories focus on atmospheric sciences (atmospheric chemistry, climate and radiation, regional processes, atmospheric modeling), hydrological sciences (snow, ice, oceans, and seasonal-to-interannual prediction), terrestrial physics (geology, terrestrial biology, land-atmosphere interactions, geophysics), climate modeling (global warming, greenhouse gases, climate change). Two of the labs also have branches that focus on sensor development especially using lidar and microwave technologies. In the information technologies, areas of scientific and technical research, and expertise include massively parallel computers, technologies for high speed electronic networks, scientific visualization, information systems, archive and distribution systems, and information processing. Examples will be presented from each of these research disciplines.

Meeson, Blanche W.↗

Mesoflow: An Open-Source Reacting Flow Solver for Catalysis at Mesoscale

We present the capabilities and software performance metrics of our open-source continuum solver for catalysis, Mesoflow, developed specifically for modeling transport and chemistry at the mesoscale. Our solver utilizes Cartesian block-structured adaptive mesh refinement to resolve complex catalyst surface morphologies directly obtained from X-ray tomography data. An immersed boundary based formulation enables rapid representation of complex geometries prevalent in most mesoporous catalyst interfaces. The solver is developed on top of open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Our flexible software framework enables integration of complex chemical mechanisms at heterogenous interfaces and time-split algorithms for circumventing highly disparate reaction and flow time-scales. Our current studies indicate a ten-fold performance gain by using graphics-processing-units (GPUs) compared to a single processor for representative problem sizes (2 million cell mesh). We will also present a brief introduction on how to build and use this software for application problems pertaining to catalytic upgrading and gas transport within porous catalyst particles.

adaptive meshing↗

Lattice cluster theory for dense, thin polymer films

While the application of the lattice cluster theory (LCT) to study the miscibility of polymer blends has greatly expanded our understanding of the monomer scale molecular details influencing miscibility, the corresponding theory for inhomogeneous systems has not yet emerged because of considerable technical difficulties and much greater complexity. Here, we present a general formulation enabling the extension of the LCT to describe the thermodynamic properties of dense, thin polymer films using a high dimension, high temperature expansion. Whereas the leading order of the LCT for bulk polymer systems is essentially simple Flory-Huggins theory, the highly non-trivial leading order inhomogeneous LCT (ILCT) for a film with L layers already involves the numerical solution of 3(L − 1) coupled, highly nonlinear equations for the various density profiles in the film. The new theory incorporates the essential “transport” constraints of Helfand and focuses on the strict imposition of excluded volume constraints, appropriate to dense polymer systems, rather than the maintenance of chain connectivity as appropriate for lower densities and as implemented in self-consistent theories of polymer adsorption at interfaces. The ILCT is illustrated by presenting examples of the computed profiles of the density, the parallel and perpendicular bonds, and the chain ends for free standing and supported films as a function of average film density, chain length, temperature, interaction with support, and chain stiffness. The results generally agree with expected general trends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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↗

Matrix product operators, matrix product states, and ab initio density matrix renormalization group algorithms

Current descriptions of the ab initio density matrix renormalization group (DMRG) algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. Here, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational parallelism. The connections and correspondences described here serve to link the future developments with the past and are important in the efficient implementation of continuing advances in ab initio DMRG and related algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

GronOR: Massively Parallel and GPU-Accelerated Non-Orthogonal Configuration Interaction for Large Molecular Systems

GronOR is a program package for non-orthogonal configuration interaction calculations for an electronic wave function built in terms of anti-symmetrized products of multi-configuration molecular fragment wave functions. The two-electron integrals that have to be processed may be expressed in terms of atomic orbitals or in terms of an orbital basis determined from the molecular orbitals of the fragments. The code has been specifically designed for execution on distributed memory massively parallel and Graphics Processing Unit (GPU)-accelerated computer architectures, using an MPI+OpenACC/OpenMP programming approach. The task-based execution model used in the implementation allows for linear scaling with the number of nodes on the largest pre-exascale architectures available, provides hardware fault resiliency, and enables effective execution on systems with distinct central processing unit-only and GPU-accelerated partitions. The code interfaces with existing multi-configuration electronic structure codes that provide optimized molecular fragment orbitals, configuration interaction coefficients, and the required integrals. Algorithm and implementation details, parallel and accelerated performance benchmarks, and an analysis of the sensitivity of the accuracy of results and computational performance to thresholds used in the calculations are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗