Qubit Condensation for Assessing Efficacy of Molecular Simulation on Quantum Computers
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Results for “Quantum chemistry simulations”
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Nonadiabatic couplings from a variational excited state method based on constrained DFT
Excited Costrained Density Functional Theory (XCDFT) is a variational excited state method that extends ground state DFT to the computation of low-lying excited states. It borrows much of the machinery of Constrained DFT (CDFT) with a crucial difference: the constraint imposes a population of one electron in the Hilbert space spanned by the virtuals of a reference ground state. Here, we present theory and implementation for evaluating nonadiabatic coupling vectors (NACVs) between the first excited state computed with XCDFT and the ground state. Our NACVs are computed analytically using density functional perturbation theory with a formalism that is general enough that could be applied to CDFT diabatic states. We showcase the new method with pilot NACV calculations for the conical intersection in H 3 , the avoided crossing in selenoacrolein, and the NACV magnitudes in azobenzene. Despite complications from the nonorthogonality of the wavefunctions, XCDFT’s energy surfaces and NACVs reproduce benchmark values and respect known sum rules within a reasonable degree. This shows that XCDFT is a viable method for nonadiabatic dynamics simulations.
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Multiscale investigation of the mechanism of biomass deconstruction in the dimethyl isosorbide/water Co-solvent pretreatment system
In the context of promoting a circular bioeconomy, the development of green and efficient lignocellulosic biomass pretreatment technologies so as to realize high value-added biomass utilization is of intense interest. We demonstrated the potential of the bio-based green solvent dimethyl isosorbide (DMI) for the fractionation of Eucalyptus biomass with excellent performance. Here, to investigate the mechanisms involved in biomass fractionation, microimaging and microspectroscopic techniques were employed together with molecular dynamics (MD) simulation and COSMO-RS quantum chemical calculations to derive multiscale information. Both the microstructure and regional chemistry of the cell wall vary significantly with the volume ratio of DMI/H 2 O. The strongest effects were found at DMI/H 2 O = 9:1 and showed visible cell wall tearing cracks and cell wall deformation and collapse as well as the lowest values of cell wall thickness and circularity. From the MD simulations, lignin exhibits collapsed-like structure in pure H 2 O with low solvent accessibility surface area (SASA) and radius of gyration (R g ). In contrast, lignin in DMI/H 2 O shows extended structure with high SASA and solvent interactions dominated by van der Waals forces, with maximal contact in the 9:1 (v/v) system. Further, the COSMO-RS calculated sigma (σ-) potential suggests the intermolecular interactions in DMI and DMI/H 2 O co-solvent are weak, leading to stronger interaction with lignin and correspondingly higher lignin dissolution. The radial distribution functions and σ-potential all show that again DMI/H 2 O at 9:1 is an optimal volume ratio for high lignin dissolution. Finally, this study provides a solvent-ratio dependent mechanism for the action of polar aprotic solvents in the deconstruction of biomass.
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Coulomb Interaction-Driven Entanglement of Electrons on Helium
The generation and evolution of entanglement in many-body systems is an active area of research that spans multiple fields, from quantum information science to the simulation of quantum many-body systems encountered in condensed matter, subatomic physics, and quantum chemistry. Motivated by recent experiments exploring quantum information processing systems with electrons trapped above the surface of cryogenic noble gas substrates, we theoretically investigate the generation of entanglement between two electrons via their unscreened Coulomb interaction. The model system consists of two electrons confined in separate electrostatic traps that establish microwave-frequency quantized states of their motion. We compute the motional energy spectra of the electrons, as well as their entanglement, by diagonalizing the model Hamiltonian with respect to a single-particle Hartree product basis. We also compare our results with the predictions of an effective Hamiltonian. The computational procedure outlined here can be employed for device design and guidance of experimental implementations. In particular, the theoretical tools developed here can be used for fine-tuning and optimization of control parameters in future experiments with electrons trapped above the surface of superfluid helium or solid neon. Published by the American Physical Society 2024
Physics↗
Applications of Quantum Chemistry to the Study of Carbon Nanotubes
For several years, scientists at NASA Ames have been studying the properties of carbon nanotubes using various experimental and computational methods. In this talk, I will compare different strategies for using quantum chemistry calculations to describe the electronic structure, deformation and chemical functionalization of single wall carbon nanotubes (SWNT) and the physisorption of small molecules on nanotube surfaces. The SWNT can be treated as an infinite (periodic) or finite length carbon cylinder or as a polycyclic aromatic hydrocarbon (PAH) molecule with an imposed curvature maintained by external constraints (as if it were cut out of the SWNT surface). Calculations are carried out using DFT and MP2 methods and a variety of atomic orbital basis sets from minimal (STO-3G) to valence triple zeta. The optimal approach is based on the particular SWNT property of interest. Examples to be discussed include: nanotube fluorination and other functionalization reactions; coating of nanotubes by water vapor and low-molecular weight organic molecules; and the nature of the interface between SWNT and liquids such as water and amines. In many cases, the quantum chemistry calculations are used to parameterize or validate force fields for molecular dynamics simulations. The results of these calculations have helped explain experimental data and contributed to the design of novel materials and sensors based on carbon nanotubes. Some of this research is described in the following papers:
Jaffe, Richard L.↗
Quantum hardware accelerators for real-world applications (CRADA FP00007654 Final Report)
The research Bleximo Corp. conducted at Cyclotron Road was focused on developing application-specific superconducting quantum processors for high-value problems in quantum chemistry, in particular, calculating chemical binding energy in biomolecular systems, such as protein-ligand complexes, with chemical precision. Several candidate architectures were identified through simulations, which included experimentally-proven quantum bit architectures and realistic noise and cross-talk models in multi-qubit superconducting quantum processors.
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Analog Quantum Simulation of the Dynamics of Open Quantum Systems with Quantum Dots and Microelectronic Circuits
We introduce a general setup for the analog quantum simulation of the dynamics of open quantum systems based on semiconductor quantum dots electrically connected to a chain of quantum RLC electronic circuits. The dots are chosen to be in the regime of spin-charge hybridization to enhance their sensitivity to the RLC circuits while mitigating the detrimental effects of unwanted noise. In this context, we establish an experimentally realizable map between the hybrid system and a qubit coupled to thermal harmonic environments of arbitrary complexity that enables the analog quantum simulation of open quantum systems. We assess the utility of the simulator by numerically exact emulations that indicate that the experimental setup can faithfully mimic the intended target even in the presence of its natural inherent noise. We further provide a detailed analysis of the physical requirements on the quantum dots and the RLC circuits needed to experimentally realize this proposal that indicates that the simulator can be created with existing technology. The approach can exactly capture the effects of highly structured non-Markovian quantum environments typical of photosynthesis and chemical dynamics, and offer clear potential advantages over conventional and even quantum computation. The proposal opens a general path for effective quantum dynamics simulations based on semiconductor quantum dots.
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Activating dislocation mediated plasticity in boron carbide through Al-doping
Dislocation slip, deformation twinning, phase transformations, and fast fracture are energy dissipation mechanisms that accommodate mechanical loading in materials. The energetically unfavorable formation of dislocation attributes to the ease of cracking and the low damage tolerance observed in superhard ceramics, notably boron carbide. Here, this work demonstrates that room temperature dislocation slip can be enabled in boron carbide by altering its chemistry through Al doping. The activation of dislocation slip is mechanistically explained by quantum mechanics simulations and electron microscopy, which indicate that strain energy is released through basal icosahedral slip facilitated by icosahedral rotation and chain bond breaking and reconfiguring. The new insight gained through this work suggests that atomic doping could be an effective strategy to tune deformation mechanisms in boron carbide, which provides a significant potential for limiting amorphization and catastrophic failure, and opens a new strategy to enhance damage tolerance in brittle ceramics.
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Nonequilibrium Solvent Effects during Photodissociation in Liquids: Dynamical Energy Surfaces, Caging and Chemical Identity
In the gas phase, potential energy surfaces can be used to provide insight into the details of photochemical reaction dynamics. In solution, however, it is unclear what potential energy surfaces, if any, can be used to describe even simple chemical reactions such as the photodissociation of a diatomic solute. In this paper, we use mixed quantum/classical (MQC) molecular dynamics (MD) to study the photodissociation of $Na$ $^+_2$ in both liquid Ar and liquid tetrahydrofuran (THF). We examine both the gas-phase potential surfaces and potentials of mean force (PMF), which assume that the solvent remains at equilibrium with the solute throughout the photodissociation process and show that neither resemble a nonequilibrium dynamical energy surface that is generated by taking the time integral of work. For the photodissociation of $Na$ $^+_2$ in liquid Ar, the dynamical energy surface shows clear signatures of solvent caging, and the degree of caging is directly related to the mass of the solvent atoms. For $Na$ $^+_2$ in liquid THF, local specific interactions between the solute and solvent lead to changes in chemical identity that create a kinetic trap that effectively prevents the molecule from dissociating. Here, the results show that nonequilibrium effects play an important role even in simple solution-phase reactions, requiring the use of dynamical energy surface to understand such chemical events.
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Li-Doped Ionic Liquid Electrolytes: From Bulk Phase to Interfacial Behavior
Ionic liquids have been proposed as candidate electrolytes for high-energy density, rechargeable batteries. We present an extensive computational analysis supported by experimental comparisons of the bulk and interfacial properties of a representative set of these electrolytes as a function of Li-salt doping. We begin by investigating the bulk electrolyte using quantum chemistry and ab initio molecular dynamics to elucidate the solvation structure of Li(+). MD simulations using the polarizable force field of Borodin and coworkers were then performed, from which we obtain an array of thermodynamic and transport properties. Excellent agreement is found with experiments for diffusion, ionic conductivity, and viscosity. Combining MD simulations with electronic structure computations, we computed the electrochemical window of the electrolytes across a range of Li(+)-doping levels and comment on the role of the liquid environment. Finally, we performed a suite of simulations of these Li-doped electrolytes at ideal electrified interfaces to evaluate the differential capacitance and the equilibrium Li(+) distribution in the double layer. The magnitude of differential capacitance is in good agreement with our experiments and exhibits the characteristic camel-shaped profile. In addition, the simulations reveal Li(+) to be highly localized to the second molecular layer of the double layer, which is supported by additional computations that find this layer to be a free energy minimum with respect to Li(+) translation.
molecular dynamics↗
Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems
The Dalton Project provides a uniform platform access to the underlying full-fledged quantum chemistry codes Dalton and LSDalton as well as the PyFraME package for automatized fragmentation and parameterization of complex molecular environments. The platform is written in Python and defines a means for library communication and interaction. Intermediate data such as integrals are exposed to the platform and made accessible to the user in the form of NumPy arrays, and the resulting data are extracted, analyzed, and visualized. Complex computational protocols that may, for instance, arise due to a need for environment fragmentation and configuration-space sampling of biochemical systems are readily assisted by the platform. The platform is designed to host additional software libraries and will serve as a hub for future modular software development efforts in the distributed Dalton community.
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Hybrid Basis and Multi-Center Grid Method for Strong-Field Processes
We present a time-dependent framework that combines a hybrid basis, consisting of Gaussian-type orbitals (GTOs) and finite-element discrete-variable representation (FEDVR) functions, with a multicenter grid to simulate strong-field and attosecond dynamics in atoms and molecules. The method incorporates the construction of the orthonormal hybrid basis, the evaluation of electronic integrals, a unitary time-propagation scheme, and the extraction of optical and photoelectron observables. Its accuracy and robustness are benchmarked on one-electron systems such as atomic hydrogen and the dihydrogen cation (H$^+_2$) through comparisons with essentially-exact reference results for bound-state energies, high-harmonic generation spectra, photoionization cross sections, and photoelectron momentum distributions. This work establishes the groundwork for its integration with quantum-chemistry methods, which is already operational but will be detailed in future work, thereby enabling ab initio simulations of correlated polyatomic systems in intense ultrafast laser fields.
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Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics
Solvation thermodynamics in molten salt is accurately and efficiently predicted by combining ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT).
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Theoretical Advances in Polariton Chemistry and Molecular Cavity Quantum Electrodynamics
When molecules are coupled to an optical cavity, new light–matter hybrid states, so-called polaritons, are formed due to quantum light–matter interactions. With the experimental demonstrations of modifying chemical reactivities by forming polaritons under strong light–matter interactions, theorists have been encouraged to develop new methods to simulate these systems and discover new strategies to tune and control reactions. This review summarizes some of these exciting theoretical advances in polariton chemistry, in methods ranging from the fundamental framework to computational techniques and applications spanning from photochemistry to vibrational strong coupling. Even though the theory of quantum light–matter interactions goes back to the midtwentieth century, the gaps in the knowledge of molecular quantum electrodynamics (QED) have only recently been filled. We review recent advances made in resolving gauge ambiguities, the correct form of different QED Hamiltonians under different gauges, and their connections to various quantum optics models. Then, we review recently developed ab initio QED approaches which can accurately describe polariton states in a realistic molecule–cavity hybrid system. We then discuss applications using these method advancements. We review advancements in polariton photochemistry where the cavity is made resonant to electronic transitions to control molecular nonadiabatic excited state dynamics and enable new photochemical reactivities. When the cavity resonance is tuned to the molecular vibrations instead, ground-state chemical reaction modifications have been demonstrated experimentally, though its mechanistic principle remains unclear. We present some recent theoretical progress in resolving this mystery. Finally, we review the recent advances in understanding the collective coupling regime between light and matter, where many molecules can collectively couple to a single cavity mode or many cavity modes. We also lay out the current challenges in theory to explain the observed experimental results. We hope that this review will serve as a useful document for anyone who wants to become familiar with the context of polariton chemistry and molecular cavity QED and thus significantly benefit the entire community.
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Chemical applications of variational quantum eigenvalue-based quantum algorithms: Perspective and survey
Exploring many-body chemical systems on classical computers often involves solving the Schrödinger equation. However, this approach is frequently limited by the exponential increase in the dimensionality of the Hamiltonian as the number of degrees of freedom increases. In contrast, quantum computing, specifically through the variational quantum eigensolver (VQE) framework, shows promise in overcoming this exponential cost. VQE can utilize the collective properties of quantum states to model the wavefunction in polynomial time. Despite the current limitations of quantum hardware, significant advances have been made in the development of VQE-based algorithms. Here, in this review, we provide an overview of emerging protocols, focusing on their applications in simulating the ground state, excited state, and vibrational properties of chemical systems. By examining notable algorithmic advancements and applications, this review aims to shed light on the challenges and potential of VQE-based algorithms in addressing relevant chemical problems.
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State predictive information bottleneck
We report the ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics simulations is heavily dependent on the knowledge of a low-dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states, and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work we propose a deep learning based state predictive information bottleneck approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is connected to the committor in chemical physics and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time delay or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations.
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Active learning for robust, high-complexity reactive atomistic simulations
Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. In this paper, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory in terms of structure, dynamics, and speciation.
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