arXiv subjects

Troy Van Voorhis

Publications and source records attributed to Troy Van Voorhis.

At least 19 recordsLinked to original sources

arXiv2026

Effective reorganization energy for electron transfer

The Marcus theory expression for the rate of non-adiabatic electron transfer is widely used across a range of physical conditions. Although Marcus theory defines the reorganization energy classically, here we show that the reorganization parameter appearing in the activation barrier for normal-region electron transfer is most generally a quantum mechanical object that depends on the electronic coupling, coinciding with the Marcus picture only in the limit of vanishing electronic coupling. This result unifies the physical description of electron-transfer activation barriers across the adiabatic and non-adiabatic regimes and formally predicts that Marcus-like rate expressions remain accurate beyond their traditional non-adiabatic domain of validity. These insights allow us to derive a closed-form expression for the curvature of the current-overpotential relation for electron-transfer-limited reactions at the electrochemical interface, now formally applicable to both inner-sphere and outer-sphere processes.

quant-ph↗

arXiv2026

Selected Coupled Cluster Guided by Selected Configuration Interaction

Coupled Cluster (CC) theory is one of the most popular correlated wavefunction methods, yet the steep computational cost scaling of CC truncated at higher rank limits its application. Modern Selected Configuration Interaction (SCI) methods successfully exploit the sparsity of ground-state wavefunctions in the determinant space, motivating an analogous approach with the exponential CC ansatz. In this work, we introduce Selected Coupled Cluster (SCC), an arbitrary-order sparse CC method that explicitly solves the CC residual equations within a compact amplitude space guided by an SCI wavefunction. SCC optimizes the CC amplitudes in the sparse amplitude space. Using a pilot Just-in-Time (JIT) compiled implementation, we benchmark SCC against SCI, SCI+PT2, standard rank-truncated dense CC (CCSD, CCSDT), and DMRG across various chemical systems. We demonstrate that SCC exhibits fast, monotonic convergence with the SCI variational threshold, outperforming SCI and surpassing SCI+PT2 in the tight-threshold regime. SCC also provides accurate energies for high-dimensional topologies, where DMRG typically converges slowly with bond dimension. SCC thus presents an efficient drop-in enhancement for modern SCI workflows.

physics.chem-ph↗

arXiv2026

Ab Initio Free Energy Surfaces for Coupled Ion-Electron Transfer

Although coupled ion-electron transfer (CIET) has emerged as a powerful framework to rationalize the kinetics of Faradaic reactions, its mechanism has lacked a fully microscopic, first-principles description. Here we show that the coupling of electron-transfer to ion-transfer can be understood as an extension of Marcus theory in which the ensemble of diabatic nuclear configurations is conditioned on a classical collective variable describing the interfacial anisotropy. This formalism enables direct construction of the CIET free-energy surface from constrained ab initio trajectories, providing a first-principles route to electrochemical current-overpotential relations. We demonstrate this method for CO2 redox on a gold electrode and find that the resulting two-dimensional saddle-point barriers differ substantially from one-dimensional treatments that consider only electron- or ion-transfer coordinates individually.

physics.chem-ph↗

arXiv2026

Modeling the Effect of C/O Ratio on Complex Carbon Chemistry in Cold Molecular Clouds

Elemental abundances, which are often depleted with respect to the solar values, are important input parameters for kinetic models of interstellar chemistry. In particular, the amount of carbon relative to oxygen is known to have a strong effect on modeled abundances of many species. While previous studies have focused on comparison of modeled and observed abundances to constrain the C/O ratio, the effects of this parameter on the underlying chemistry have not been well-studied. We investigated the role of the C/O ratio on dark cloud chemistry using the NAUTILUS code and machine learning techniques for molecular representation. We find that modeled abundances are quite sensitive to the C/O ratio, especially for carbon-rich species such as carbon chains and polycyclic aromatic hydrocarbons (PAHs). CO and simple ice-phase species are found to be major carbon reservoirs under both oxygen-poor and oxygen-rich conditions. The appearance of C3H4 isomers as significant carbon reservoirs, even under oxygen-rich conditions, indicates the efficiency of gas-phase C3 formation followed by adsorption and grain-surface hydrogenation. Our model is not able to reproduce the observed, gas-phase C/H ratio of TMC-1 CP at the time of best fit with any C/O ratio between 0.1 and 3, suggesting that the modeled freeze-out of carbon-bearing molecules may be too rapid. Future investigations are needed to understand the reactivity of major carbon reservoirs and their conversion to complex organic molecules.

astro-ph.GA↗

arXiv2025

How to use quantum computers for biomolecular free energies

Free energy calculations are at the heart of physics-based analyses of biochemical processes. They allow us to quantify molecular recognition mechanisms, which determine a wide range of biological phenomena from how cells send and receive signals to how pharmaceutical compounds can be used to treat diseases. Quantitative and predictive free energy calculations require computational models that accurately capture both the varied and intricate electronic interactions between molecules as well as the entropic contributions from motions of these molecules and their aqueous environment. However, accurate quantum-mechanical energies and forces can only be obtained for small atomistic models, not for large biomacromolecules. Here, we demonstrate how to consistently link accurate quantum-mechanical data obtained for substructures to the overall potential energy of biomolecular complexes by machine learning in an integrated algorithm. We do so using a two-fold quantum embedding strategy where the innermost quantum cores are treated at a very high level of accuracy. We demonstrate the viability of this approach for the molecular recognition of a ruthenium-based anticancer drug by its protein target, applying traditional quantum chemical methods. As such methods scale unfavorable with system size, we analyze requirements for quantum computers to provide highly accurate energies that impact the resulting free energies. Once the requirements are met, our computational pipeline FreeQuantum is able to make efficient use of the quantum computed energies, thereby enabling quantum computing enhanced modeling of biochemical processes. This approach combines the exponential speedups of quantum computers for simulating interacting electrons with modern classical simulation techniques that incorporate machine learning to model large molecules.

quant-ph↗

arXiv2025

All Or Nothing: No-Downfolding Theorems For Quantum Simulation

The physics of a quantum system with many degrees of freedom is often approximated by downfolding: most of the degrees of freedom are "folded into" a much smaller number of degrees of freedom, resulting in an effective Hamiltonian that still captures the essential physics. Approaches of this sort are particularly relevant for quantum information, where exact downfolding would allow eigenstates in a large Hilbert space to be simulated with fewer qubits. Very little work has been done to prove the existence of such an exact downfolding for general systems or even particular cases. In this letter we prove that exact quantum downfolding is impossible for what is perhaps the most commonly-used formulation of the task. Specifically, for a non-trivial Hamiltonian $\mathbf{H}(x)\equiv\mathbf{A}+x\mathbf{B}$ that depends on some parameter $x$ (e.g. an electric field, bond length or interaction strength) it is not possible to construct a lower-dimension effective Hamiltonian $\mathbf{h}(x)\equiv\mathbf{a}+x\mathbf{b}$ that exactly recovers \emph{any} of the eigenvalue functions $E_i(x)$ of $\mathbf{H}(x)$. We discuss several generalizations of this result and the impacts of these findings on future directions in quantum information.

quant-ph↗

arXiv2025

Structural Hole Traps in III-V Quantum Dots

Non-toxic III-V quantum dots (QDs) are plagued with a higher density of performance-limiting trap states than II-VI and IV-VI QDs. Such trap states are generally understood to arise from under-coordinated atoms on the QD surface. Here, we present computational evidence for, and an exploration of, trap states in InP and GaP QDs that arise from fully-coordinated atoms with distorted geometries, denoted here as structural traps. In particular, we focus on the properties of anion-centered hole traps, which we show to be relatively insensitive to the choice of the (typically cation-coordinating) ligand. Through interpolation of trap center cutouts, we arrive at a simple molecular orbital (MO) argument for the existence of structural traps, finding two main modalities: bond stretches and angular distortion to a see-saw-like geometry. These structural trap states will be important for understanding the low performance of III-V QDs, as even core-shell passivation may not remove these defects unless they can rigidify the structure. Moreover, they may lead to interesting dynamical properties as distorted structures could form transiently.

cond-mat.mtrl-sci↗

arXiv2025

Multiscale Embedding for Quantum Computing

We present a novel multi-scale embedding scheme that links conventional QM/MM embedding and bootstrap embedding (BE) to allow simulations of large chemical systems on limited quantum devices. We also propose a mixed-basis BE scheme that facilitates BE calculations on extended systems using classical computers with limited memory resources. Benchmark data suggest the combination of these two strategies as a robust path in attaining the correlation energies of large realistic systems, combining the proven accuracy of BE with chemical and biological systems of interest in a lower computational cost method. Due to the flexible tunability of the resource requirements and systematic fragment construction, future developments in the realization of quantum computers naturally offer improved accuracy for multi-scale BE calculations.

physics.chem-ph↗

arXiv2025

Unification of Finite Symmetries in Simulation of Many-body Systems on Quantum Computers

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems can results in significant overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems; however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry group transforms on quantum computers to simulate many-body systems. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed matter physics and \textit{ab initio} electronic structure in quantum chemistry. Specifically, we execute a symmetry-adapted quantum subroutine for small molecules in first-quantization on noisy hardware. In addition, we present a discussion of open problems regarding treating symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries \emph{quantumly} for practical quantum advantage. The broad applicability and rigorous resource estimation for symmetry transformations make our framework appealing for achieving provable quantum advantage on fault-tolerant quantum computers, especially for symmetry-related properties.

quant-ph↗

arXiv2025

Clifford circuit based heuristic optimization of fermion-to-qubit mappings

Simulation of interacting fermionic Hamiltonians is one of the most promising applications of quantum computers. However, the feasibility of analysing fermionic systems with a quantum computer hinges on the efficiency of fermion-to-qubit mappings that encode non-local fermionic degrees of freedom in local qubit degrees of freedom. While recent works have highlighted the importance of designing fermion-to-qubit mappings that are tailored to specific problem Hamiltonians, the methods proposed so far are either restricted to a narrow class of mappings or they use computationally expensive and unscalable brute-force search algorithms. Here, we address this challenge by designing a $\mathrm{\textbf{heuristic}}$ numerical optimization framework for fermion-to-qubit mappings. To this end, we first translate the fermion-to-qubit mapping problem to a Clifford circuit optimization problem, and then use simulated annealing to optimize the average Pauli weight of the problem Hamiltonian. For all fermionic Hamiltonians we have considered, the numerically optimized mappings outperform their conventional counterparts, including ternary-tree-based mappings that are known to be optimal for single creation and annihilation operators. We find that our optimized mappings yield between $15\%$ to $40\%$ improvements on the average Pauli weight when the simulation Hamiltonian has an intermediate level of complexity. Most remarkably, the optimized mappings improve the average Pauli weight for $6 \times 6$ nearest-neighbor hopping and Hubbard models by more than $40\%$ and $20\%$, respectively. Surprisingly, we also find specific interaction Hamiltonians for which the optimized mapping outperform $\mathrm{\textbf{any}}$ ternary-tree-based mapping. Our results establish heuristic numerical optimization as an effective method for obtaining mappings tailored for specific fermionic Hamiltonian.

quant-ph↗

arXiv2025

High ground state overlap via quantum embedding methods

Quantum computers can accurately compute ground state energies using phase estimation, but this requires a guiding state that has significant overlap with the true ground state. For large molecules and extended materials, it becomes difficult to find guiding states with good ground state overlap for growing molecule sizes. Additionally, the required number of qubits and quantum gates may become prohibitively large. One approach for dealing with these challenges is to use a quantum embedding method, which allows a reduction to one or multiple smaller quantum cores embedded in a larger quantum region. In such situations it is unclear how the embedding method affects the hardness of constructing good guiding states. In this work, we therefore investigate the preparation of guiding states in the context of quantum embedding methods. We extend previous work on quantum impurity problems, a framework in which we can rigorously analyze the embedding of a subset of orbitals. While there exist results for optimal active orbital space selection in terms of energy minimization, we rigorously demonstrate how the same principles can be used to define selected orbital spaces for state preparation in terms of the overlap with the ground state. Moreover, we perform numerical studies of molecular systems relevant to biochemistry, one field in which quantum embedding methods are required due to the large size of biomacromolecules such as proteins and nucleic acids. We investigate two different embedding strategies which can exhibit qualitatively different orbital entanglement. In all cases we demonstrate that the easy-to-obtain mean-field state will have a sufficiently high overlap with the target state to perform quantum phase estimation.

quant-ph↗

arXiv2024

Sensitivity Analysis of Aromatic Chemistry to Gas-Phase Kinetics in a Dark Molecular Cloud Model

The increasingly large number of complex organic molecules detected in the interstellar medium necessitates robust kinetic models that can be relied upon for investigating the involved chemical processes. Such models require rate constants for each of the thousands of reactions; the values of these are often estimated or extrapolated, leading to large uncertainties that are rarely quantified. We have performed a global Monte Carlo and a more local one-at-a-time sensitivity analysis on the gas-phase rate coefficients in a 3-phase dark cloud model. Time-dependent sensitivities have been calculated using four metrics to determine key reactions for the overall network as well as for the cyanonaphthalene molecule in particular, an important interstellar species that is severely under-produced by current models. All four metrics find that reactions involving small, reactive species that initiate hydrocarbon growth have large effects on the overall network. Cyanonaphthalene is most sensitive to a number of these reactions as well as ring-formation of the phenyl cation (C6H5+) and aromatic growth from benzene to naphthalene. Future efforts should prioritize constraining rate coefficients of key reactions and expanding the network surrounding these processes. These results highlight the strength of sensitivity analysis techniques to identify critical processes in complex chemical networks, such as those often used in astrochemical modeling.

astro-ph.GA↗

arXiv2024

Triplet Exciton Sensitization of Silicon Mediated by Defect States in Hafnium Oxynitride

Singlet exciton fission has the potential to increase the efficiency of crystalline silicon solar cells beyond the conventional single junction limit. Perhaps the largest obstacle to achieving this enhancement is uncertainty about energy coupling mechanisms at the interfaces between silicon and exciton fission materials such as tetracene. Here, the previously reported silicon-hafnium oxynitride-tetracene structure is studied and a combination of magnetic-field-dependent silicon photoluminescence measurements and density functional theory calculations is used to probe the influence of the interlayer composition on the triplet transfer process across the hafnium oxynitride interlayer. It is found that hafnium oxide interlayers do not show triplet exciton sensitization of silicon, and that nitrogen content in hafnium oxynitride layers is correlated with enhanced sensitization. Calculation results reveal that defects in hafnium oxynitride interlayers with higher nitrogen content introduce states close to the band-edge of silicon, which can mediate the triplet exciton transfer process. Some defects introduce additional deleterious mid-gap states, which may explain observed silicon photoluminescence quenching. These results show that band-edge states can mediate the triplet exciton transfer process, potentially through a sequential charge transfer mechanism.

cond-mat.mtrl-sci↗

arXiv2024

Exciton Fission Enhanced Silicon Solar Cell

While silicon solar cells dominate global photovoltaic energy production, their continued improvement is hindered by the single junction limit. One potential solution is to use molecular singlet exciton fission to generate two electrons from each absorbed high-energy photon. We demonstrate that the long-standing challenge of coupling molecular excited states to silicon solar cells can be overcome using sequential charge transfer. Combining zinc phthalocyanine, aluminum oxide, and a shallow junction crystalline silicon microwire solar cell, the peak charge generation efficiency per photon absorbed in tetracene is (138 +- 6)%, comfortably surpassing the quantum efficiency limit for conventional silicon solar cells and establishing a new, scalable approach to low cost, high efficiency photovoltaics.

cond-mat.mtrl-sci↗

arXiv2024

Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals

Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme. As an illustrative example, we calculated the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the wB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems.

physics.chem-ph↗

arXiv2024

Understanding Trap States in InP and GaP Quantum Dots Through Density Functional Theory

The widespread application of III-V colloidal quantum dots (QDs) as non-toxic, highly tunable emitters is stymied by their high density of trap states. Here, we utilize density functional theory (DFT) to investigate trap state formation in a diverse set of realistically passivated core-only InP and GaP QDs. Through orbital localization techniques, we deconvolute the dense manifold of trap states to allow for detailed assignment of surface defects. We find that the three-coordinate species dominate trapping in III-V QDs and identify features in the geometry and charge environment of trap centers capable of deepening, or sometimes passivating, traps. Furthermore, we observe stark differences in surface reconstruction between InP and GaP, where the more labile InP reconstructs to passivate three-coordinate indium at the cost of distortion elsewhere. These results offer explanations for experimentally observed trapping behavior and suggest new avenues for controlling trap states in III-V QDs.

cond-mat.mtrl-sci↗

arXiv2024

MP2-based composite extrapolation schemes can predict core-ionization energies for first-row elements with coupled-cluster level accuracy

X-ray photoelectron spectroscopy (XPS) measures core-electron binding energies (CEBEs) to reveal element-specific insights into chemical environment and bonding. Accurate theoretical CEBE prediction aids XPS interpretation but requires proper modeling of orbital relaxation and electron correlation upon core-ionization. This work systematically investigates basis set selection for extrapolation to the complete basis set (CBS) limit of CEBEs from $Δ$MP2 and $Δ$CC energies across 94 K-edges in diverse organic molecules. We demonstrate that an alternative composite scheme using $Δ$MP2 in a large basis corrected by $Δ$CC-$Δ$MP2 difference in a small basis can quantitatively recover optimally extrapolated $Δ$CC CEBEs within 0.02 eV. Unlike $Δ$CC, MP2 calculations do not suffer from convergence issues and are computationally cheaper, and, thus, the composite $Δ$MP2/$Δ$CC scheme balances accuracy and cost, overcoming limitations of solely using either method. We conclude by providing a comprehensive analysis of the choice of small and large basis sets for the composite schemes and provide practical recommendations for highly accurate (within 0.10-0.15 eV MAE) ab initio prediction of XPS spectra.

physics.chem-ph↗

arXiv2024

Broad Yet Narrow: Super-resolution techniques to simulate electronic spectra of large molecular systems

An accurate treatment of electronic spectra in large systems with a technique such as time dependent density functional theory (TDDFT) is computationally challenging. Due to the Nyquist sampling theorem, direct real time simulations must be prohibitively long in order to recover a suitably sharp resolution in frequency space. Super-resolution techniques such as compressed sensing and MUSIC assume only a small number of excitations contribute to the spectrum, which fails in large molecular systems where the number of excitations is typically very large. We present a new approach that combines exact short time dynamics with approximate frequency space methods to capture large narrow features embedded in a dense manifold of smaller nearby peaks. We show that our approach can accurately capture narrow features and broad quasi-continuum of states at the same time - even when the features overlap in frequency. Our approach is able reduce the required simulation time by a factor of 20-40 with respect to standard Fourier analysis and shows promise for the accurate whole-spectrum prediction of large molecules and materials.

cond-mat.mtrl-sci↗