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At least 667 records · Page 37

Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Ionic solvation phenomena in liquids involve intense interactions in the inner solvation shell. For interactions beyond the first shell, the ion–solvent interaction energies result from the sum of many smaller-magnitude contributions that can still include polarization effects. Deep neural network (DNN) methods have recently found wide application in developing efficient molecular models that maintain near-quantum accuracy. In this work, we extend the DeePMD-kit code to produce accurate molecular multipole moments in the bulk and near interfaces. The new method is validated by comparing the DNN moments with those generated by ab initio simulations. The moments are used to compute the electrostatic potential at the center of a molecular-sized hydrophobic cavity in water. The results show that the fields produced by the DNN models are in quantitative agreement with the AIMD-derived values. These efficient methods will open the door to more accurate solvation models for large solutes such as proteins.

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Generalized quantum master equations can improve the accuracy of semiclassical predictions of multitime correlation functions

Multitime quantum correlation functions are central objects in physical science, offering a direct link between the experimental observables and the dynamics of an underlying model. While experiments such as 2D spectroscopy and quantum control can now measure such quantities, the accurate simulation of such responses remains computationally expensive and sometimes impossible, depending on the system’s complexity. A natural tool to employ is the generalized quantum master equation (GQME), which can offer computational savings by extending reference dynamics at a comparatively trivial cost. However, dynamical methods that can tackle chemical systems with atomistic resolution, such as those in the semiclassical hierarchy, often suffer from poor accuracy, limiting the credence one might lend to their results. By combining work on the accuracy-boosting formulation of semiclassical memory kernels with recent work on the multitime GQME, here we show for the first time that one can exploit a multitime semiclassical GQME to dramatically improve both the accuracy of coarse mean-field Ehrenfest dynamics and obtain orders of magnitude efficiency gains.

Chemistry↗

Estimates of Quantum Tunneling Effects for Hydrogen Diffusion in PuO 2

We detail the estimation of activation energies and quantum nuclear vibrational tunneling effects for hydrogen diffusion in PuO 2 based on Density Functional Theory calculations and a quantum double well approximation. We find that results are relatively insensitive to choice of exchange correlation functional. In addition, the representation of spin in the system and use of an extended Hubbard U correction has only a small effect on hydrogen point defect formation energies when the PuO 2 lattice is held fixed at the experimental density. We then compute approximate activation energies for transitions between hydrogen interstitial sites seeded by a semi-empirical quantum model and determine the quantum tunneling enhancement relative to classical kinetic rates. Our model indicates that diffusion rates in H/PuO 2 systems could be enhanced by more than one order of magnitude at ambient conditions and that these effects persist at high temperature. The method we propose here can be used as a fast screening tool for assessing possible quantum nuclear vibrational effects in any number of condensed phase materials and surfaces, where hydrogen hopping tends to follow well defined minimum energy pathways.

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Zero-Dimensional Hybrid Organic–Inorganic Indium Bromide with Blue Emission

Low-dimensional hybrid organic–inorganic metal halides have received increased attention because of their outstanding optical and electronic properties. However, the most studied hybrid compounds contain lead and have long-term stability issues, which must be addressed for their use in practical applications. In this work, we report a new zero-dimensional hybrid organic–inorganic halide, RInBr 4 , featuring photoemissive trimethyl(4-stilbenyl)methylammonium (R + ) cations and nonemissive InBr 4 – tetrahedral anions. The crystal structure of RInBr 4 is composed of alternating layers of inorganic anions and organic cations along the crystallographic a axis. The resultant hybrid demonstrates bright-blue emission with Commission Internationale de l’Eclairage color coordinates of (0.19, 0.20) and a high photoluminescence quantum yield (PLQY) of 16.36% at room temperature, a 2-fold increase compared to the PLQY of 8.15% measured for the precursor organic salt RBr. On the basis of our optical spectroscopy and computational work, the organic component is responsible for the observed blue emission of the hybrid material. In addition to the enhanced light emission efficiency, the novel hybrid indium bromide demonstrates significantly improved environmental stability. These findings may pave the way for the consideration of hybrid organic In(III) halides for light emission applications.

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Mechanism and Dynamics of Photodecarboxylation Catalyzed by Lactate Monooxygenase

Photoenzymes are a rare class of biocatalysts that use light to facilitate chemical reactions. Many of these catalysts utilize a flavin cofactor to absorb light, suggesting that other flavoproteins might have latent photochemical functions. Lactate monooxygenase is a flavin-dependent oxidoreductase previously reported to mediate the photodecarboxylation of carboxylates to afford alkylated flavin adducts. While this reaction holds a potential synthetic value, the mechanism and synthetic utility of this process are unknown. Here, we combine femtosecond spectroscopy, site-directed mutagenesis, and a hybrid quantum-classical computational approach to reveal the active site photochemistry and the role the active site amino acid residues play in facilitating this decarboxylation. Light-induced electron transfer from histidine to flavin was revealed, which has not been reported in other proteins. These mechanistic insights enable the development of catalytic oxidative photodecarboxylation of mandelic acid to produce benzaldehyde, a previously unknown reaction for photoenzymes. Furthermore, our findings suggest that a much wider range of enzymes have the potential for photoenzymatic catalysis than has been realized to date.

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Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.

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Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. Further, this physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. In conclusion, not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.

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The primary gas phase hydration shell of hydroxide

The number of water molecules in hydroxide’s primary hydration shell has been long debated to be three from the interpretation of experimental data and four from theoretical studies. Here, we provide direct evidence for the presence of a fourth water molecule in hydroxide’s primary hydration shell from a combined study based on high-resolution cryogenic experimental photoelectron spectroscopy and high-level quantum chemical computations. Well-defined spectra of OH - (H 2 O) n clusters (n = 2 to 5) yield accurate electron binding energies, which are, in turn, used as key signatures of the underlying molecular conformations. Although the smaller OH - (H 2 O) 3 and OH - (H 2 O) 4 clusters adopt close-lying conformations with similar electron binding energies that are hard to distinguish, the OH - (H 2 O) 5 cluster clearly has a predominant conformation with a four-coordinated hydroxide binding motif, a finding that unambiguously determines the gas phase coordination number of hydroxide to be four.

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Experimental and theoretical studies of the doubly substituted methyl-ethyl Criegee intermediate: Infrared action spectroscopy and unimolecular decay to OH radical products

In this work the infrared (IR) action spectrum of the doubly substituted methyl-ethyl Criegee intermediate (MECI) is observed in the CH stretch overtone region with detection of OH products. The MECI exhibits four conformers, all of which undergo unimolecular decay via a 1,4 H-atom transfer mechanism, followed by the rapid release of OH products. Conformers with different orientations of the carbonyl oxide group with respect to the methyl and ethyl substituents (i.e., anti and syn ) decay via distinct transition state barriers (16.1 kcal mol –1 and 15.4 kcal mol –1 , respectively). The observed IR action spectrum is in good agreement with the predicted anharmonic IR absorption spectrum, but exhibits significant congestion, which is attributed to couplings between spectroscopic bright states and nearby dark states. Energy-dependent OH appearance rates are measured upon IR excitation of the strongest features in the IR action spectrum and are found to be on the order of 10 6 –10 7 s –1 . The experimental rates are in good agreement with computed Rice–Ramsperger–Kassel–Marcus rates for the unimolecular decay of MECI at these energies, which incorporate quantum mechanical tunneling and sophisticated hindered rotor treatments, as well as high-level theoretical calculations of the TS barrier heights, rovibrational properties, and torsional barriers associated with the MECI conformers. Master equation modeling is used to predict thermal rates for the unimolecular decay of anti - and syn -MECI of 473 s –1 and 660 s –1 , respectively. Comparison with other previously studied Criegee intermediate systems provides insights into substituent effects on unimolecular decay under both energy-dependent and thermal conditions.

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Theoretical studies of chemical reactions related to the formation and growth of polycyclic aromatic hydrocarbons (PAH) and molecular properties of their key intermediates (Final Progress Report)

The formation mechanisms of polycyclic aromatic hydrocarbons, (PAHs) – organic molecules carrying fused benzene rings – are of great interest to scientists and engineers due to their importance in combustion chemistry and astrochemistry. On Earth, PAHs are largely produced in incomplete combustion of fossil fuel and are considered as critical precursors to unwanted soot particles leading to combustion inefficiency and causing air pollution along with detrimental health effects. Simple PAH molecules initially formed in the gas phase, are further involved in a build-up process in combustion flames leading to larger PAH, bowl-shaped nanostructures, fullerenes, and solid-phase species including carbonaceous dust, graphene particles, and soot. In deep space, PAH and their derivatives are potential key intermediates and nucleation sites leading eventually to carbonaceous nanoparticles (“interstellar grains”). Therefore, the understanding of the key processes in the synthesis of PAHs along with their precursors and their degradation mechanisms in combustion systems and in interstellar, circumstellar, and planetary atmospheric environments will provide critical insights into how complex aromatic structures, carbonaceous nanoparticles, and fullerenes are formed and destroyed. Achieving this understanding is an important step in the development of the efficient combustion processes and of the ecofriendly devices with reduced environmental pollution as well as technological strategies for the production of hydrogen and solid carbon through thermal or plasma-assisted pyrolysis of natural gas and biomass. Also, the understanding of the key processes of PAH and soot growth will help in our comprehension of chemical evolution in the universe. Detailed information on the mechanisms and reliable rate constants of the key elementary chemical reactions involved in PAH formation and destruction processes and in inception of soot particles is often missing, with the main deficiencies being the absence of temperature- and pressure-dependent rate constants for the broad range of conditions occurring in various terrestrial and interstellar processes and the lack of data on the reaction products and their branching ratios. Complementary to experimental studies, these gaps in knowledge can be filled by using quantum chemical calculations of reaction potential energy surfaces providing us with accurate energies of reaction products, intermediates, and transition states, revealing the reaction mechanism, and giving the molecular properties required to compute rate constants for relevant reaction steps and product branching ratios using the RRKM-Master Equation (ME) method. Molecular dynamics (MD) simulations can be used in cases when a reaction rate cannot be properly described by statistical theories. During the terminal renewal project period we employed these ab initio/RRKM-ME and MD approaches to complete our studies on several key reactions relevant to the formation/growth of PAH and inception of soot particles including (1) the reaction mechanism and kinetics of the resonance stabilized fulvenallenyl radical with propargyl and C 3 H 4 isomers; (2) the reaction mechanism and kinetics for the C + indene and C 2 + styrene reactions producing naphthyl or azulenyl radicals in low-temperature environments; (3) the MD study of non-equilibrium dimerization of acepyrene and coronene and its radical. The information derived from our theoretical calculations contributed to a better fundamental understanding of the reaction mechanisms and provide missing critical kinetic data to improve combustion models of hydrocarbon fuels and astrochemical models of the growth of carbonaceous molecules and particles in cold molecular clouds, circumstellar envelopes, and planetary atmospheres.

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Is the Trotterized UCCSD Ansatz Chemically Well-Defined?

The variational quantum eigensolver (VQE) has emerged as one of the most enticing near-term quantum algorithms that can be used to simulate many-body systems such as molecular electronic structures. Serving as an attractive ansatz in the VQE algorithm, unitary coupled cluster (UCC) theory has seen a renewed interest in recent literature. Yet, unlike the original classical UCC theory, implementation on a quantum computer requires a finite-order Suzuki-Trotter decomposition to separate the exponentials of the large sum of Pauli operators. While previous literature has recognized the non-uniqueness of different orderings of the operators in the Trotterized form of UCC methods, the question of whether or not different orderings matter at the chemical scale has not been addressed. In this letter, we explore the effect of operator ordering on the Trotterized UCCSD ansatz, as well as the much more compact k-UpCCGSD ansatz recently proposed by Lee et al. We observe a significant, system-dependent variation in the energies of Trotterizations with different operator orderings. The energy variations occur on a chemical scale, sometimes on the order of hundreds of kcal/mol. This letter establishes the need to define not only the operators present in the ansatz, but also the order in which they appear. This is necessary for adhering to the quantum chemical notion of a ``model chemistry'', in addition to the general importance of scientific reproducibility. As a final note, we suggest a useful strategy to select out of the combinatorial number of possibilities, a single well-defined and effective ordering of the operators.

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Dinuclear Complexes of Uranyl, Neptunyl, and Plutonyl: Structures and Oxidation States Revealed by Experiment and Theory

Dinuclear perchlorate complexes of uranium, neptunium, and plutonium were characterized by reactivity and DFT, with results revealing structures containing pentavalent, hexavalent, and heptavalent actinyls, and actinyl-actinyl interactions (AAIs). Electrospray ionization produced native complexes [(AnO 2 ) 2 (ClO 4 ) 3 ] - for An:An = U:U, Np:Np, Pu:Pu, and Np:Pu, which are intuitively formulated as actinyl(V) perchlorates. However, DFT identified lower-energy structures [(AnO 2 )(AnO 3 )(ClO 4 ) 2 (ClO 3 )] - comprising a perchlorate fragmented to ClO 3 , actinyl(VI) cation An VI O 2 2+ , and neutral AnO 3 . For U:U and Np:Np, and Np in Np:Pu, the coordinated AnO 3 is calculated as actinyl(VI) with an equatorial oxo, [O yl =An VI =O yl ][=O eq ], whereas for Pu:Pu, it is plutonyl(V) oxyl, [O yl =Pu V =O yl ][-O eq • ]. The implied lower stability of Pu VI versus Np VI indicates weaker Pu=O eq versus Np=O eq bonding. Adsorption of O 2 by the U:U complex suggests oxidation of U V to U VI , corroborating the assignment of perchlorate [(An V O 2 ) 2 (ClO 4 ) 3 ] - . DFT predicts the O 2 adducts are [(An VI O 2 )(O 2 )(An VI O 2 )(ClO 4 ) 3 ] - with actinyls oxidized from +V to +VI by bridging peroxide, O 2 2- . In accordance with reactivity, O 2- addition is computed as substantially exothermic for U:U and least favorable for Pu:Pu. Collision-induced dissociation of native complexes eliminated ClO 2 to yield [(AnO 2 )(O) 2 (AnO 2 )(ClO 4 ) 2 ] - , in which fragmented O atoms bridge as oxyl O -• and oxo O 2- to yield uranyl(VI) and plutonyl(VI), or as oxos O 2- to yield neptunyl(VII), [O yl =Np VII =O yl ] 3+ .

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Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗

Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry

Low-cost, non-empirical corrections to semi-local density functional theory are essential for accurately modeling transition-metal chemistry. Here, we demonstrate the judiciously modified density functional theory (jmDFT) approach with non-empirical U and J parameters obtained directly from frontier orbital energetics on a series of transition-metal complexes. We curate a set of nine representative Ti(III) and V(IV) d 1 transition-metal complexes and evaluate their flat-plane errors along the fractional spin and charge lines. We demonstrate that while jmDFT improves upon both DFT+U and semi-local DFT with the standard atomic orbital projectors (AOPs), it does so inefficiently. We rationalize these inefficiencies by quantifying hybridization in the relevant frontier orbitals. To overcome these limitations, we introduce a procedure for computing a molecular orbital projector (MOP) basis for use with jmDFT. We demonstrate this single set of d 1 MOPs to be suitable for nearly eliminating all energetic delocalization and static correlation errors. In all cases, MOP jmDFT outperforms AOP jmDFT, and it eliminates most flat-plane errors at non-empirical values. Unlike DFT+U or hybrid functionals, jmDFT nearly eliminates energetic delocalization and static correlation errors within a non-empirical framework.

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Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results

Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+

Chemistry↗

Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces

Here, this paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C 5 H 5 using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree–Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.

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Space-Time Quantum Information from the Entangled States of Magnetic Molecule (STI Product)

This collaborative project combines synthesis, measurement, and theory by three faculty members at the Eddleman Quantum Institute of UC Irvine to effectively investigate the quantum properties of molecules in the space, time, and frequency domains. Through synthetic chemistry, molecules are tailored for their magnetic and coherent properties. By combining femtosecond (fs) terahertz (THz) light and a continuous wave (cw) THz laser with a low temperature scanning tunneling microscope (STM), quantum phenomena are probed with simultaneous femtosecond temporal and atomic-scale spatial resolution. In particular, the invention of the quantum superposition microscope (QSM) advances quantum sensing for enhanced spectroscopy and imaging capabilities. Coupling theory to the experimental efforts offers a deeper understanding and predictive power for the molecular systems. The phenomena of superposition, entanglement, and coherence is central to quantum information science and can be realized in qubit states. Many systems can be modeled by a double-well potential in which two levels are formed in the two lowest energy states interacting with the environment and external radiation. In focusing on molecules as two-level systems, the underlying expectation is that their tunable composition and structure allows an effective parameter space to optimize their use as qubits for quantum sensing and computing. The THz radiation induces the superposition between the two states, appearing as temporal oscillations that damp in amplitude. Enhanced spectroscopy and imaging in the time and frequency domains is achieved through the extreme sensitivity of the frequency and damping of coherence of two-level systems to its environment. A single hydrogen molecule trapped in the STM tunneling gap experiences a double-well potential and absorption of THz femtosecond pulses of light creates the superposition of its two levels, appearing as damped oscillations in the light induced direct current (DC). The oscillation frequency depends sensitively on the electric field distribution of the copper nitride (Cu 2 N) surface, through the Stark effect, and associated with the different charge distributions at the copper and nitrogen sites and in between. This QSM can resolve variation in the surface electric field with 0.02 nanometer resolution. In addition, the single hydrogen molecule entaes with nearby hydrogen molecules as seen in the avoided level crossings of energy (oscillation frequency) versus the voltage across the tunneling gap. Thus, the first application of the QSM senses and images the surface electric field at the atomic scale. Results from this project advance fundamental understanding of quantum phenomena, develop novel synthesis, measurement, and theory, provide the knowledge foundation for molecule-based qubits and sensing that enable the development of the QSM and emergent technologies. This project trained researchers in quantum information science, extended knowledge in classrooms, and outreached to the community.

47 OTHER INSTRUMENTATION↗

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Abstract Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

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