Small Ester Combustion Chemistry: Computational Kinetics and Experimental Study of Methyl Acetate and Ethyl Acetate
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Aces4: A Platform for Computational Chemistry Calculations with Extremely Large Block-Sparse Arrays
Aces4 is a parallel programming platform comprising a DSL for Computational Chemistry and its runtime system. It offers a convenient way to express parallelism together with extensive support for extremely large, possibly sparse, distributed arrays. It aids scientists in the creation of performant, scalable, massively parallel programs that can effectively take advantage of leadership class computing systems to address important scientific questions. Aces4 has enabled the development and implementation of new methods in electronic structure theory which are breaking new ground in their ability to perform highly accurate calculations on ever larger molecular systems. In this paper the design of Aces4, which is based on the the Super Instruction Architecture approach, is described. Experimental scaling results for Molecular Cluster Perturbation Theory, a new method enabled by Aces4, and CCSD, a widely used computational chemistry method are given.
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COMET: A Domain-Specific Compilation of High-Performance Computational Chemistry
The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations. These simulations have often been tied to one platform and restricted in generality by the interface provided to the user. The expanding prevalence of accelerators and researcher demands necessitate a more general approach which is not tied to specific hardware or requires contortion of algorithms to specific hardware platforms. In this paper we present COMET, a domain-specific programming language and compiler infrastructure for tensor contractions targeting heterogeneous accelerators. We present a system of progressive lowering through multiple layers of abstraction and optimization that achieves up to 1.98×speedup for 30 tensor contractions commonly used in computational chemistry and beyond.
Mutlu, Erdal↗
Surface Interactions and Confinement of Methane: A High Pressure Magic Angle Spinning NMR and Computational Chemistry Study
Characterization and modeling of the molecular-level behavior of simple hydrocarbon gases, such as methane, in the presence of both nonporous and nano-porous mineral matrices allows for predictive understanding of important processes in engineered and natural systems. In this study, changes in local electromagnetic environments of the carbon atoms in methane under conditions of high pressure (up to 130 bar) and moderate temperature (up to 346 K) were observed with 13C magic-angle spinning (MAS) NMR spectroscopy while the methane gas was mixed with two model solid substrates: a fumed non-porous, 12 nm particle size silica and a mesoporous silica with 200 nm particle size and 4 nm average pore diameter. Examination of the interactions between methane and the silica systems over temperatures and pressures that include the supercritical regime was allowed by a novel high pressure MAS sample containment system, which provided high resolution spectra collected under in situ conditions. For pure methane, no significant thermal effects were found for the observed 13C chemical shifts at all pressures studied here (28.2 bar, 32.6 bar, 56.4 bar, 65.1 bar, 112.7 bar, and 130.3 bar). However, the 13C chemical shifts of resonances arising from confined methane changed slightly with changes in temperature in mixtures with mesoporous silica. The chemical shift values of 13C nuclides in methane change measurably as a function of pressure both in the pure state and in mixtures with both silica matrices, with a more pronounced shift when meso-porous silica is present. Molecular-level simulations utilizing GCMC, MD and DFT confirm qualitatively that the experimentally measured changes are attributed to interactions of methane with the hydroxylated silica surfaces as well as densification of methane within nanopores and on pore surfaces.
confinement↗
cclib 2.0: An updated architecture for interoperable computational chemistry
Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots—the formalization of cclib’s intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.
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Single-Site Zeolite-Anchored Organoiridium Carbonyl Complexes: Characterization of Structure and Reactivity by Spectroscopy and Computational Chemistry
Abstract not provided
Martinez-Macias, Claudia↗
Developing a Computational Chemistry Framework for the Exascale Era
Within computational chemistry the NWChem package has arguably been the de facto standard for running high-accuracy numerical simulations on the most powerful supercomputers. In order to better address the challenges presented by emerging exascale architectures, the decision has been made to rewrite NWChem. Design of the resulting package, NWChemEx, has been driven by exascale computing; however, significant additional design considerations have arisen from the team's involvement with the Molecular Sciences Software Institute (MolSSI). MolSSI is a National Science Foundation initiative focused on establishing coding and data standards for the computational chemistry community. As a result, NWChemEx is built upon a general computational chemistry framework called the Simulation Development Environment (SDE) that is designed with a focus on extensibility and interoperability. The present manuscript describes the modular approach of the SDE and how it has been used to implement the self-consistent field algorithm within NWChemEx.
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Computational chemistry
With the advent of supercomputers, modern computational chemistry algorithms and codes, a powerful tool was created to help fill NASA's continuing need for information on the properties of matter in hostile or unusual environments. Computational resources provided under the National Aerodynamics Simulator (NAS) program were a cornerstone for recent advancements in this field. Properties of gases, materials, and their interactions can be determined from solutions of the governing equations. In the case of gases, for example, radiative transition probabilites per particle, bond-dissociation energies, and rates of simple chemical reactions can be determined computationally as reliably as from experiment. The data are proving to be quite valuable in providing inputs to real-gas flow simulation codes used to compute aerothermodynamic loads on NASA's aeroassist orbital transfer vehicles and a host of problems related to the National Aerospace Plane Program. Although more approximate, similar solutions can be obtained for ensembles of atoms simulating small particles of materials with and without the presence of gases. Computational chemistry has application in studying catalysis, properties of polymers, all of interest to various NASA missions, including those previously mentioned. In addition to discussing these applications of computational chemistry within NASA, the governing equations and the need for supercomputers for their solution is outlined.
Arnold, J. O.↗
A Case for Soft Error Detection and Correction in Computational Chemistry
High performance computing platforms are expected to deliver 10(18) floating operations per second by the year 2022 through the deployment of millions of cores. Even if every core is highly reliable the sheer number of the them will mean that the mean time between failures will become so short that most applications runs will suffer at least one fault. In particular soft errors caused by intermittent incorrect behavior of the hardware are a concern as they lead to silent data corruption. In this paper we investigate the impact of soft errors on optimization algorithms using Hartree-Fock as a particular example. Optimization algorithms iteratively reduce the error in the initial guess to reach the intended solution. Therefore they may intuitively appear to be resilient to soft errors. Our results show that this is true for soft errors of small magnitudes but not for large errors. We suggest error detection and correction mechanisms for different classes of data structures. The results obtained with these mechanisms indicate that we can correct more than 95% of the soft errors at moderate increases in the computational cost.
Computational chemistry↗
From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape
Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.
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Design of Chemoresponsive Liquid Crystals through Integration of Computational Chemistry and Experimental Studies
We report the use of computational chemistry methods to design a chemically responsive liquid crystal (LC). Specifically, we used electronic structure calculations to model the binding of nitrile-containing mesogens (4'-n-pentyl-4-biphenylcarbonitrile) to metal perchlorate salts (with explicit description of the perchlorate anion), which we call the coordinately saturated anion model (CSAM). The model results were validated against experimental data. We then used the CSAM to predict that selective fluorination can reduce the strength of binding of nitrile-containing nematic LCs to metal-salt-decorated surfaces and thus generate a faster reordering of the LC in response to competitive binding of dimethylmethylphosphonate (DMMP). We tested this prediction via synthesis of fluorinated compounds 3-fluoro-4'-pentyl[1,1'-biphenyl]-4-carbonitrile and 4-fluoro-4'-pentyl-1,1'-biphenyl, and subsequent experimental measurements of the orientational response of LCs containing these compounds to DMMP. In conclusion, these experimental measurements confirmed the theoretical predictions, thus providing the first demonstration of a chemoresponsive LC system designed from computational chemistry.
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On the Impact of Execution Models: A Case Study in Computational Chemistry
Efficient utilization of high-performance computing (HPC) platforms is an important and complex problem. Execution models, abstract descriptions of the dynamic runtime behavior of the execution stack, have significant impact on the utilization of HPC systems. Using a computational chemistry kernel as a case study and a wide variety of execution models combined with load balancing techniques, we explore the impact of execution models on the utilization of an HPC system. We demonstrate a 50 percent improvement in performance by using work stealing relative to a more traditional static scheduling approach. We also use a novel semi-matching technique for load balancing that has comparable performance to a traditional hypergraph-based partitioning implementation, which is computationally expensive. Using this study, we found that execution model design choices and assumptions can limit critical optimizations such as global, dynamic load balancing and finding the correct balance between available work units and different system and runtime overheads. With the emergence of multi- and many-core architectures and the consequent growth in the complexity of HPC platforms, we believe that these lessons will be beneficial to researchers tuning diverse applications on modern HPC platforms, especially on emerging dynamic platforms with energy-induced performance variability.
Execution models, computational chemistry, MPI and↗
TChem ? An Open Source Computational Chemistry Software Library for Heterogeneous Computing Platforms.
Abstract not provided.
Safta, Cosmin↗
TChem - An Open Source Computational Chemistry Software Library for Heterogenous Computing Platforms.
Abstract not provided.
Safta, Cosmin↗
The role of computational chemistry in the science and measurements of the atmosphere
The role of computational chemistry in determining the stability, photochemistry, spectroscopic parameters, and parameters for estimating reaction rates of atmospheric constituents is discussed. Examples dealing with the photolysis cross sections of HOCl and (1 Delta g) O2 and with the stability of gaseous NH4Cl and asymmetric ClO3 are presented. It is concluded that computational chemistry can play an important role in the study of atmospheric constituents, particularly reactive and short-lived species which are difficult to investigate experimentally.
Phillips, D. H.↗
Deep learning for computational chemistry
The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.
Computational Chemistry↗
Computational chemistry for NH 3 synthesis, hydrotreating, and NO x reduction: Three topics of special interest to Haldor Topsøe
Computational chemistry, especially density functional theory, has experienced a remarkable growth in terms of application over the last few decades. This is attributed to the improvements in theory and computing infrastructure that enable the analysis of systems of unprecedented size and detail at an affordable computational expense. In this perspective, we discuss recent progress and current challenges facing electronic structure theory in the context of heterogeneous catalysis. We specifically focus on the impact of computational chemistry in elucidating and designing catalytic systems in three topics of interest to Haldor Topsøe – ammonia, synthesis, hydrotreating, and NO x reduction. Furthermore, we then discuss the common tools and concepts in computational catalysis that underline these topics and provide a perspective on the challenges and future directions of research in this area of catalysis research.
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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.
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