Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “quantum chemical computation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

Revised Models of Interstellar Nitrogen Isotopic Fractionation

Nitrogen-bearing molecules in cold molecular clouds exhibit a range of isotopic fractionation ratios and these molecules may be the precursors of 15N enrichments found in comets and meteorites. Chemical model calculations indicate that atom–molecular ion and ion–molecule reactions could account for most of the fractionation patterns observed. However, recent quantum-chemical computations demonstrate that several of the key processes are unlikely to occur in dense clouds. Related model calculations of dense cloud chemistry show that the revised 15N enrichments fail to match observed values. We have investigated the effects of these reaction rate modifications on the chemical model of Wirström et al. (2012) for which there are significant physical and chemical differences with respect to other models. We have included 15N fractionation of CN in neutral–neutral reactions and also updated rate coefficients for key reactions in the nitrogen chemistry. We find that the revised fractionation rates have the effect of suppressing 15N enrichment in ammonia at all times, while the depletion is even more pronounced, reaching 14N/15N ratios of >2000. Taking the updated nitrogen chemistry into account, no significant enrichment occurs in HCN or HNC, contrary to observational evidence in dark clouds and comets, although the 14N/15N ratio can still be below 100 in CN itself. However, such low CN abundances are predicted that the updated model falls short of explaining the bulk 15N enhancements observed in primitive materials. It is clear that alternative fractionating reactions are necessary to reproduce observations, so further laboratory and theoretical studies are urgently needed.

astrochemistry – molecular processes – comets: gen↗

Revised Models of Interstellar Nitrogen Isotopic Fractionation

Nitrogen-bearing molecules in cold molecular clouds exhibit a range of isotopic fractionation ratios and these molecules may be the precursors of (15)N enrichments found in comets and meteorites. Chemical model calculations indicate that atom-molecular ion and ion-molecule reactions could account for most of the fractionation patterns observed. However, recent quantum-chemical computations demonstrate that several of the key processes are unlikely to occur in dense clouds. Related model calculations of dense loud chemistry show that the revised (15)N enrichments fail to match observed values. We have investigated the effects of these reaction rate modifications on the chemical model of Wirstrom et al. (2012) for which there are significant physical and chemical differences with respect to other models. We have included (15)N fractionation of CN in neutral-neutral reactions and also updated rate coefficients for key reactions in the nitrogen chemistry. We find that the revised fractionation rates have the effect of suppressing (15)N enrichment in ammonia at all times, while the depletion is even more pronounced, reaching (14)N/(15)N ratios of > 2000. Taking the updated nitrogen chemistry into account, no sigificant enrichment occurs in HCN or HNC, contrary to observational evidence in dark clouds and comets, although the (14)N/(15)N ratio can still be below 100 in CN itself. However, such low CN abundances are predicted that the updated model falls short of explaining the bulk (15)N enhancements observed in primitive materials. It is clear that alternative fractionating reactions are necessary to reproduce observations, so further laboratory and theoretical studies are urgently needed.

Astrochemistry↗

Revised Models of Interstellar Nitrogen Isotopic Fractionation

Nitrogen-bearing molecules in cold molecular clouds exhibit a range of isotopic fractionation ratios and these molecules may be the precursors of 15N enrichments found in comets and meteorites. Chemical model calculations indicate that atom-molecular ion and ion-molecule reactions could account for most of the fractionation patterns observed. However, recent quantum-chemical computations demonstrate that several of the key processes are unlikely to occur in dense clouds. Related model calculations of dense cloud chemistry show that the revised 15N enrichments fail to match observed values. We have investigated the effects of these reaction rate modfications on the chemical model of Wirstrom et al. (2012) for which there are significant physical and chemical differences with respect to other models. We have included 15N fractionation of CN in neutral-neutral reactions and also updated rate coefficients for key reactions in the nitrogen chemistry. We find that the revised fractionation rates have the effect of suppressing 15N enrichment in ammonia at all times, while the depletion is even more pronounced, reaching 14N/15N ratios of > 2000. Taking the updated nitrogen chemistry into account, no significant enrichment occurs in HCN or HNC, contrary to observational evidence in dark clouds and comets, although the 14N/15N ratio can still be below 100 in CN itself. However, such low CN abundances are predicted that the updated model falls short of explaining the bulk 15N enhancements observed in primitive materials. It is clear that alternative fractionating reactions are necessary to reproduce observations, so further laboratory and theoretical studies are urgently needed.

astrochemistry – molecular processes – comets: gen↗

Error detection on quantum computers improving the accuracy of chemical calculations

The ultimate goal of quantum error correction is to achieve the fault-tolerance threshold beyond which quantum computers can be made arbitrarily accurate. This requires extraordinary resources and engineering efforts. We show that even without achieving full fault-tolerance, quantum error detection is already useful on the current generation of quantum hardware. We demonstrate this by executing an end-to-end chemical calculation for the hydrogen molecule encoded in the [[4, 2, 2]] quantum error-detecting code. The encoded calculation with logical qubits significantly improves the accuracy of the molecular ground-state energy.

97 MATHEMATICS AND COMPUTING↗

Understanding flavin electronic structure and spectra

Abstract Flavins have emerged as central to electron bifurcation, signaling, and countless enzymatic reactions. In bifurcation, two electrons acquired as a pair are separated in coupled transfers wherein the energy of both is concentrated on one of the two. This enables organisms to drive demanding reactions based on abundant low‐grade chemical fuel. To enable incorporation of this and other flavin capabilities into designed materials and devices, it is essential to understand fundamental principles of flavin electronic structure that make flavins so reactive and tunable by interactions with protein. Emerging computational tools can now replicate spectra of flavins and are gaining capacity to explain reactivity at atomistic resolution, based on electronic structures. Such fundamental understanding can moreover be transferrable to other chemical systems. A variety of computational innovations have been critical in reproducing experimental properties of flavins including their electronic spectra, vibrational signatures, and nuclear magnetic resonance (NMR) chemical shifts. A computational toolbox for understanding flavin reactivity moreover must be able to treat all five oxidation and protonation states, in addition to excited states that participate in flavoprotein's light‐driven reactions. Therefore, we compare emerging hybrid strategies and their successes in replicating effects of hydrogen bonding, the surrounding dielectric, and local electrostatics. These contribute to the protein's ability to modulate flavin reactivity, so we conclude with a survey of methods for incorporating the effects of the protein residues explicitly, as well as local dynamics. Computation is poised to elucidate the factors that affect a bound flavin's ability to mediate stunningly diverse reactions, and make life possible. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Spectroscopy

59 BASIC BIOLOGICAL SCIENCES↗

Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansätze for Hybrid Variational Quantum Computing

One of the commonly used chemical-inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansatze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansatze exhibits unfavorable scaling with respect to the system size, hindering its practical use on near-term quantum devices. Efforts have been taken to propose some variants of UCC ansatze with better scaling. In this paper we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansatze employing spin-adapted formulation, small amplitude filtration, and entropy-based orbital selection approaches. Numerical results of using our approach on some small molecules have exhibited a significant cost reduction in the number of parameters to be optimized and in the time to convergence compared with conventional UCCSD-VQE simulations. Further, we also discuss the potential application of some machine learning techniques in further exploring the parameter redundancy, providing a possible direction for future studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Classical Preoptimization Approach for ADAPT-VQE: Maximizing the Potential of High-Performance Computing Resources to Improve Quantum Simulation of Chemical Applications

The ADAPT-VQE algorithm is a promising method for generating a compact ansatz based on derivatives of the underlying cost function, and it yields accurate predictions of electronic energies for molecules. In this work, we report the implementation and performance of ADAPT-VQE with our recently developed sparse wave function circuit solver (SWCS) in terms of accuracy and efficiency for molecular systems with up to 52 spin orbitals. The SWCS can be tuned to balance computational cost and accuracy, which extends the application of ADAPT-VQE for molecular electronic structure calculations to larger basis sets and a larger number of qubits. Using this tunable feature of the SWCS, we propose an alternative optimization procedure for ADAPT-VQE to reduce the computational cost of the optimization. Furthermore, by preoptimizing a quantum simulation with a parametrized ansatz generated with ADAPT-VQE/SWCS, we aim to utilize the power of classical high-performance computing in order to minimize the work required on noisy intermediate-scale quantum hardware, which offers a promising path toward demonstrating quantum advantage for chemical applications.

ADAPT-VQE↗

Computed potential energy surfaces for chemical reactions

Quantum mechanical methods have been used to compute potential energy surfaces for chemical reactions. The reactions studied were among those believed to be important to the NASP and HSR programs and included the recombination of two H atoms with several different third bodies; the reactions in the thermal Zeldovich mechanism; the reactions of H atom with O2, N2, and NO; reactions involved in the thermal De-NO(x) process; and the reaction of CH(squared Pi) with N2 (leading to 'prompt NO'). These potential energy surfaces have been used to compute reaction rate constants and rates of unimolecular decomposition. An additional application was the calculation of transport properties of gases using a semiclassical approximation (and in the case of interactions involving hydrogen inclusion of quantum mechanical effects).

Walch, Stephen P.↗

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models

Efficient methods for searching the chemical space of molecular compounds are needed to automate and accelerate the design of new functional molecules such as pharmaceuticals. Given the high cost in both resources and time for experimental efforts, computational approaches play a key role in guiding the selection of promising molecules for further investigation. Here, we construct a workflow to accelerate design by combining approximate quantum chemical methods [i.e. density-functional tight-binding (DFTB)], a graph convolutional neural network (GCNN) surrogate model for chemical property prediction, and a masked language model (MLM) for molecule generation. Property data from the DFTB calculations are used to train the surrogate model; the surrogate model is used to score candidates generated by the MLM. The surrogate reduces computation time by orders of magnitude compared to the DFTB calculations, enabling an increased search of chemical space. Furthermore, the MLM generates a diverse set of chemical modifications based on pre-training from a large compound library. We utilize the workflow to search for near-infrared photoactive molecules by minimizing the predicted HOMO-LUMO gap as the target property. Our results show that the workflow can generate optimized molecules outside of the original training set, which suggests that iterations of the workflow could be useful for searching vast chemical spaces in a wide range of design problems.

Blanchard, Andrew↗

Quantum chemical calculations of interatomic potentials for computer simulation of solids

A comprehensive mathematical model by which the collective behavior of a very large number of atoms within a metal or alloy can accurately be simulated was developed. Work was done in order to predict and modify the strength of materials to suit our technological needs. The method developed is useful in studying atomic interactions related to dislocation motion and crack extension.

Source record↗

Quantum Computing Algorithms and Applications for Coherent and Strongly Correlated Chemical Systems

This project advanced quantum algorithms, quantum information theory, strongly correlated electronic structure methods, molecular quantum materials, and exciton transport imaging in coherent condensed phase systems. Across the award period, the team developed new methods for open-quantum-system simulation, Hamiltonian learning, state tomography, reduced-density-matrix and contracted-quantum eigensolver approaches, and quantum diagnostics for device capability and openness.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The basics of quantum computing for chemists

The rapid and successful strides in quantum chemistry in the past decades can be largely credited to a conspicuous synergy between theoretical and computational advancements. However, the architectural computer archetype that enabled such a progress is approaching a state of more stagnant development. One of the most promising technological avenues for the continuing progress of quantum chemistry is the emerging quantum computing paradigm. This revolutionary proposal comes with several challenges, which span a wide array of disciplines. In chemistry, it implies, among other things, a need to reformulate some of its long established cornerstones in order to adjust to the operational demands and constraints of quantum computers. Due to its relatively recent emergence, much of quantum computing may still seem fairly nebulous and largely unknown to most chemists. It is in this context that here we review and illustrate the basic aspects of quantum information and their relation to quantum computing insofar as enabling simulations of quantum chemistry. As a result, we consider some of the most relevant developments in light of these aspects and discuss the current landscape when of relevance to quantum chemical simulations in quantum computers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Prepare Ground States of Highly Frustrated Magnetic Clusters on Quantum Computers

Solving challenging problems in physical, chemical, and materials sciences is one of the most promising applications of quantum utility that can be realized on current noisy hardware, considering (i) the direct map (encoding) from the quantum particles and their interactions to the qubits and their entangling gates and (ii) the rapidly improved quantum hardware and advanced error-mitigation techniques. Understanding quantum spin liquid in frustrated magnetic materials is a longstanding challenge in condensed matter physics and the nature of the ground-state phases is highly debated among researchers. Using IBM quantum computers with superconducting qubits, we implemented a variational quantum eigensolver (VQE) algorithm to prepare the ground states of two 12-site cluster approximations of these highly frustrated magnetic materials. The interaction graphs of the two corresponding Hamiltonians are (a) the six-pointed star graph (a unit cell of the kagome lattice) and (b) the cuboctahedral graph (the kagome on a sphere). These are also two instances of Quantum Max Cut problem. With the VQE based on the Hamiltonian variational ansatz acting on a valence bond solid initial trial state, we prepared the ground states and obtained the exact ground energy on simulator and high accuracy on noisy hardware. The deep ansatz necessary to reach the ground state of the cuboctahedral graph indicates that it is a hard instance of Quantum Max Cut.

Wang, Yan↗

A workflow for automatic generation and efficient refinement of individual pressure-dependent networks

Manual chemical kinetic model construction often requires modelists to at least implicitly guess all possible decay pathways and their relative fluxes for all important species. We present a workflow and associated tool for automatic generation and re finement of pressure-dependent networks that should enable modelists to efficiently and comprehensively identify decay pathways and estimate respective parameters for chemical species. This tool combines the capabilities of the Reaction Mechanism Generator (RMG) software to generate possible reaction paths, determine thermochemistry, approximate rate coefficients and estimate frequencies with the capabilities of the Arkane software to use quantum chemical parameters to compute thermochemistry and pressure dependent rates. A flux-based algorithm is used to decide which isomers to add to the network. Isomers added to the network are reacted to form other channels. When enough of the flux is accounted for by the isomers and bimolecular product channels, the generation process terminates to yield a comprehensive network. Network sensitivity analysis is applied to the network to identify the most important wells and barriers that can be refined using quantum chemistry calculations. Iterative refinement can then be used to achieve accuracy. The comprehensive network can be easily reduced using energy and flux-based algorithms to the most important channels and isomers in the network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spectroscopic Constants and Vibrational Frequencies for l-C3H+ and Isotopologues from Highly-Accurate Quartic Force Fields: The Detection of l-C3H+ in the Horsehead Nebula PDR Questioned

Very recently, molecular rotational transitions observed in the photon-dominated region of the Horsehead nebula have been attributed to l-C3H+. In an effort to corroborate this finding, we employed state-of-the art and proven high-accuracy quantum chemical techniques to compute spectroscopic constants for this cation and its isotopologues. Even though the B rotational constant from the fit of the observed spectrum and our computations agree to within 20 MHz, a typical level of accuracy, the D rotational constant differs by more than 40%, while the H rotational constant differs by three orders of magnitude. With the likely errors in the rotational transition energies resulting from this difference in D on the order of 1 MHz for the lowest observed transition (J = 4 yields 3) and growing as J increases, the assignment of the observed rotational lines from the Horsehead nebula to l-C3H+ is questionable.

ISM: individual objects↗

Solvent Stabilization of Protic Oxonium/Ammonium Intermediates in Cation Radical Cyclization Reactions Investigated via Computational Approaches

Anodic intramolecular cyclization reactions have substantial synthetic utility for formation of cyclic carbon–carbon or carbon–heteroatom bonds. For cases of intramolecular trapping of a cation radical by a protic nucleophile, the cyclization step coincides with a substantial increase in substrate acidity and thus may exhibit particularly pronounced solvent effects. In this computational work, we employ both quantum chemical (QM) and quantum mechanics/molecular mechanics (QM/MM) methods to compute solvent effects on free energy profiles for cyclization and deprotonation reaction steps for cation radical intermediates of substrates representative for anodic intramolecular cyclizations. We find substantial solvent contribution to the thermodynamic driving force for cation radical cyclization; for example, methanol and tetrahydrofuran solvents provide ∼30–35 kJ/mol driving force to form cyclic oxonium cation radicals and ∼15–25 kJ/mol driving force to form cyclic ammonium cation radicals, compared to baseline reactions in dichloromethane solvent. Given that these solvent shifts are on par with the innate cyclization reaction thermodynamics, the choice of solvent plays a crucial role in promoting/driving the cation radical cyclization step. Methanol is particularly effective at facilitating rapid deprotonation of the cyclic cation radical intermediate, which may lead to the full electrochemical process (e.g., second electron transfer) proceeding heterogeneously at the anode.

Khan, Shahriar [ORNL] (ORCID:0000000289138430)↗

Bringing chemical structures to life with augmented reality, machine learning, and quantum chemistry

Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or protein data bank ID and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗