Seyyed Mehdi Hosseini Jenab, Brandon Henderson, Scott N. Genin
We introduce a parallel, GPU-accelerated implementation of the iterative qubit coupled cluster (iQCC) method that overcomes the exponential growth of the transformed Hamiltonian -- the principal bottleneck for classical emulation of quantum chemistry circuits. By distributing Hamiltonian terms across compute nodes via bit-wise partitioning and offloading Pauli contractions to GPUs, we achieve speedups exceeding two orders of magnitude over the serial CPU approach. Crucially, iQCC confines the variational evolution to a classically simulable operator subspace by selecting entanglers exclusively from the Direct Interaction Space, which guarantees non-vanishing energy gradients at every iteration and thereby naturally avoids the barren-plateau phenomenon that renders highly expressive quantum circuits untrainable. Leveraging these algorithmic and hardware advances, we simulate electronic-structure Hamiltonians for industrially relevant ruthenium catalysts in the 100--124 qubit regime, completing full ground-state calculations on NVIDIA GPUs in the ranges of 1.2 - 45 hrs and surpassing the accuracy of Density Matrix Renormalization Group. These results effectively de-quantize a significant portion of the NISQ roadmap: quantum advantage for chemistry is often assumed to emerge beyond ${\sim}50$ qubits, yet our work demonstrates that this frontier lies significantly further -- potentially past 200 qubits -- reshaping expectations for where genuine quantum advantage may first appear.
Yifan Tang, Gian Marcello Andolina, Alica Cuzzocrea
et al.
Recent years have witnessed a surge of experimental and theoretical interest in controlling the properties of matter, such as its chemical reactivity, by confining it in optical cavities, where the enhancement of the light-matter coupling strength leads to the creation of hybrid light-matter states known as polaritons. However, ab initio calculations that account for the quantum nature of both the electromagnetic field and matter are challenging and have only started to be developed in recent years. We introduce a deep learning variational quantum Monte Carlo method to solve the electronic and photonic Schrödinger equation of molecules trapped in optical cavities. We extend typical electronic neural network wavefunction ansatzes to describe joint fermionic and bosonic systems, i.e. electron-photon systems, in a quantum Monte Carlo framework. We apply our method to hydrogen molecules in a cavity, computing both ground and excited states. We assess their energy, dipole moment, charge density shift due to the cavity, the state of the photonic field, and the entanglement developed between the electrons and photons. When possible, we compare our results with more conventional quantum chemistry methods proposed in the literature, finding good qualitative agreement, thus extending the range of scientific problems that can be tackled using machine learning techniques.
A recent observation of the exoplanet K2-18b sparked interest among scientists - large amounts of carbon dioxide and methane were detected in an H2-rich background atmosphere. If the planet is a hycean world (liquid water ocean + hydrogen-dominated atmosphere), it could be habitable under certain conditions. The presence of carbon, hydrogen and oxygen was already confirmed, however, there was no detection of nitrogen or its compounds. Molecular nitrogen is difficult to detect directly. This study concentrates on possible photochemical products of N2 such as HCN, NH3 and HC3N. We set approximate limits on the amount of nitrogen bearing species by varying atmospheric parameters, such as the Eddy Diffusion coefficient and the amount of N2 present from 10 ppm to 10%. If the bulk nitrogen-containing gas in the atmosphere is N2, photochemistry produces only trace amounts of the aforementioned species. However, if ammonia is the main source of nitrogen, then the quantities of NH3, CH5N and HCN approach detectable range. HC3N and NO are bad tracers of the nitrogen source in the atmosphere, because they are produced in similar amounts in all tested scenarios. Assuming equilibrium chemistry at the surface of K2-18b results in underprediction of CO2 abundance. This result combined with the non-detection of ammonia by JWST suggests the planet is not a typical sub-Neptune, but could be indeed a hycean world or magma ocean planet. We also found that C2H6 is produced in significant amounts - if it is detected in the future, it could serve as a proxy for DMS presence.
Niccolò Bancone, Rosangela Santalucia, Stefano Pantaleone
et al.
Understanding the interaction between hydrogen cyanide (HCN) and silicate surfaces is crucial for elucidating the prebiotic processes occurring on interstellar grain cores, as well as in cometary and meteoritic matrices. In this study, we characterized the adsorption features of HCN on crystalline forsterite (Mg2SiO4) surfaces, one of the most abundant cosmic silicates, by combining experimental infrared spectra at low temperatures (100-150 K) with periodic DFT simulations. Results showed the coexistence of both molecular and dissociative HCN adsorption complexes as a function of the considered forsterite crystalline face. Molecular adsorptions dominate on the most stable surfaces, while dissociative adsorptions occur predominantly on surfaces of lower stability, catalyzed by the enhanced Lewis acid-base behavior of surface-exposed Mg2+-O2- ion pairs. On the whole set of adsorption cases, harmonic frequency calculations were carried out and compared with the experimental infrared bands. To disentangle each vibrational mode contributing to the experimental broad bands, we run a best non-linear fit between the predicted set of frequencies and the experimental bands. The outcome of this procedure allowed us to: i) deconvolute the experimental IR spectrum by assigning computed normal modes of vibrations to the main features of each band; ii) reveal which crystal faces are responsible of the largest contribution to the adsorbate vibrational bands, giving information about the morphology of the samples. The present straigthforward procedure is quite general and of broad interest in the fine characterization of the infrared spectra of adsorbates on complex inorganic material surfaces.
Sándor Demes, Cheikh Tidiane Bop, Malek Ben Khalifa
et al.
Recent astronomical observations revealed an increasing molecular complexity in the interstellar medium through the detection of a series of large cyclic carbon species. To correctly interpret these detections, a complex analysis is necessary that takes into account the non-local thermodynamic equilibrium (non-LTE) conditions of the emitting media (e.g. when energy level populations deviate from a Boltzman distribution). This requires proper state-to-state collisional data for the excitation and de-excitation processes of the molecular levels. Cyclopentadiene (c-C5H6), which was recently detected in multiple cold interstellar clouds, is extensively studied in many aspects due to its large importance for chemistry in general. At the same time, there are no collisional data available for this species, which are necessary for a more precise interpretation of the corresponding detections. In this work, we first provide an accurate 3D rigid-rotor interaction potential for the [c-C5H6 + He] complex from high-level of ab initio theories, which has been used to study their inelastic collision by the exact close coupling quantum scattering method. To the best of our knowledge, this is the first study where this method is systematically applied to treat the dynamics of molecular collisions involving more than ten atoms. We also analyse the collisional propensity rules and the differences in contrast to calculations, where the approximate coupled states scattering methods is used.
Nicholas J. Williams, Lara Kabalan, Ljiljana Stojanovic
et al.
A significant challenge in computational chemistry is developing approximations that accelerate \emph{ab initio} methods while preserving accuracy. Machine learning interatomic potentials (MLIPs) have emerged as a promising solution for constructing atomistic potentials that can be transferred across different molecular and crystalline systems. Most MLIPs are trained only on energies and forces in vacuum, while an improved description of the potential energy surface could be achieved by including the curvature of the potential energy surface. We present Hessian QM9, the first database of equilibrium configurations and numerical Hessian matrices, consisting of 41,645 molecules from the QM9 dataset at the $ω$B97x/6-31G* level. Molecular Hessians were calculated in vacuum, as well as water, tetrahydrofuran, and toluene using an implicit solvation model. To demonstrate the utility of this dataset, we show that incorporating second derivatives of the potential energy surface into the loss function of a MLIP significantly improves the prediction of vibrational frequencies in all solvent environments, thus making this dataset extremely useful for studying organic molecules in realistic solvent environments for experimental characterization.
David Peter Kovacs, Ilyes Batatia, Eszter Sara Arany
et al.
The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems from amorphous carbon, universal materials modelling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimisation to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient, and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.
Daniel Paul, Manfred Grieser, Florian Grussie
et al.
Observations of CH$^+$ are used to trace the physical properties of diffuse clouds, but this requires an accurate understanding of the underlying CH$^+$ chemistry. Until this work, the most uncertain reaction in that chemistry was dissociative recombination (DR) of CH$^+$. Using an electron-ion merged-beams experiment at the Cryogenic Storage Ring, we have determined the DR rate coefficient of the CH$^+$ electronic, vibrational, and rotational ground state applicable for different diffuse cloud conditions. Our results reduce the previously unrecognized order-of-magnitude uncertainty in the CH$^+$ DR rate coefficient to $\sim \pm 20\%$ and are applicable at all temperatures relevant to diffuse clouds, ranging from quiescent gas to gas locally heated by processes such as shocks and turbulence. Based on a simple chemical network, we find that DR can be an important destruction mechanism at temperatures relevant to quiescent gas. As the temperature increases locally, DR can continue to be important up to temperatures of $ \sim 600\,\mathrm{K} $ if there is also a corresponding increase in the electron fraction of the gas. Our new CH$^+$ DR rate coefficient data will increase the reliability of future studies of diffuse cloud physical properties via CH$^+$ abundance observations.
I. Gallardo Cava, V. Bujarrabal, J. Alcolea
et al.
Context. There is a class of binary post-asymptotic giant branch (post-AGB) stars that exhibit remarkable near-infrared (NIR) excess. Such stars are surrounded by Keplerian or quasi-Keplerian disks, as well as extended outflows composed of gas escaping from the disk. This class can be subdivided into disk- and outflow-dominated sources, depending on whether it is the disk or the outflow that represents most of the nebular mass, respectively. The chemistry of this type of source has been practically unknown thus far. Methods. We focused our observations on the 1.3, 2, 3 mm bands of the 30 m IRAM telescope and on the 7 and 13 mm bands of the 40 m Yebes telescope. Our observations add up around 600 hours of telescope time. Results. We present the first single-dish molecular survey of mm-wave lines in nebulae around binary post-AGB stars. We conclude that the molecular content is relatively low in nebulae around binary post-AGB stars, as their molecular lines and abundances are especially weaker compared with AGB stars. This fact is very significant in those sources where the Keplerian disk is the dominant component of the nebula. The study of their chemistry allows us to classify nebulae around AC Her, the Red Rectangle, AI CMi, R Sct, and IRAS 20056+1834 as O-rich, while that of 89 Her is probably C-rich. The calculated abundances of the detected species other than CO are particularly low compared with AGB stars. The initial stellar mass derived from the 17O/18O ratio for the Red Rectangle and 89 Her is compatible with the central total stellar mass derived from previous mm-wave interferometric maps. The very low 12CO/13CO ratios found in binary post-AGB stars reveal a high 13CO abundance compared to AGB and other post-AGB stars.
Shota Notsu, Ewine F. van Dishoeck, Catherine Walsh
et al.
Recent water line observations toward several low-mass protostars suggest low water gas fractional abundances in the inner warm envelopes. Water destruction by X-rays has been proposed to influence the water abundances in these regions, but the detailed chemistry, including the nature of alternative oxygen carriers, is not yet understood. In this study, we aim to understand the impact of X-rays on the composition of low-mass protostellar envelopes, focusing specifically on water and related oxygen bearing species. We compute the chemical composition of two low-mass protostellar envelopes using a 1D gas-grain chemical reaction network, under various X-ray field strengths. According to our calculations, outside the water snowline, the water gas abundance increases with $L_{\mathrm{X}}$. Inside the water snowline, water maintains a high abundance of $\sim 10^{-4}$ for small $L_{\mathrm{X}}$, with water and CO being the dominant oxygen carriers. For large $L_{\mathrm{X}}$, the water gas abundances significantly decrease just inside the water snowline (down to $\sim10^{-8}-10^{-7}$) and in the innermost regions ($\sim10^{-6}$). For these cases, the O$_{2}$ and O gas abundances reach $\sim 10^{-4}$ within the water snowline, and they become the dominant oxygen carriers. The HCO$^{+}$ and CH$_{3}$OH abundances, which have been used as tracers of the water snowline, significantly increase/decrease within the water snowline, respectively, as the X-ray fluxes become larger. The abundances of some other dominant molecules, such as CO$_{2}$, OH, CH$_{4}$, HCN, and NH$_{3}$, are also affected by strong X-ray fields, especially within their own snowlines. These X-ray effects are larger in lower density envelope models. Future observations of water and related molecules (using e.g., ALMA and ngVLA) will access the regions around protostars where such X-ray induced chemistry is effective.
Alexander J. McCaskey, Zachary P. Parks, Jacek Jakowski
et al.
We present a quantum chemistry benchmark for noisy intermediate-scale quantum computers that leverages the variational quantum eigensolver, active space reduction, a reduced unitary coupled cluster ansatz, and reduced density purification as error mitigation. We demonstrate this benchmark on the 20 qubit IBM Tokyo and 16 qubit Rigetti Aspen processors via the simulation of alkali metal hydrides (NaH, KH, RbH),with accuracy of the computed ground state energy serving as the primary benchmark metric. We further parameterize this benchmark suite on the trial circuit type, the level of symmetry reduction, and error mitigation strategies. Our results demonstrate the characteristically high noise level present in near-term superconducting hardware, but provide a relevant baseline for future improvement of the underlying hardware, and a means for comparison across near-term hardware types. We also demonstrate how to reduce the noise in post processing with specific error mitigation techniques. Particularly, the adaptation of McWeeny purification of noisy density matrices dramatically improves accuracy of quantum computations, which, along with adjustable active space, significantly extends the range of accessible molecular systems. We demonstrate that for specific benchmark settings, the accuracy metric can reach chemical accuracy when computing over the cloud on certain quantum computers.
Young stars emit strong flares of X-ray radiation that penetrate the surface layers of their associated protoplanetary disks. It is still an open question as to whether flares create significant changes in disk chemical composition. We present models of the time-evolving chemistry of gas-phase water during X-ray flaring events. The chemistry is modeled at point locations in the disk between 1 and 50 au at vertical heights ranging from the mid-plane to the surface. We find that strong, rare flares, i.e., those that increase the unattenuated X-ray ionization rate by a factor of 100 every few years, can temporarily increase the gas-phase water abundance relative to H can by more than a factor of $\sim3-5$ along the disk surface (Z/R $\ge$ 0.3). We report that a "typical" flare, i.e., those that increase the unattenuated X-ray ionization rate by a factor of a few every few weeks, will not lead to significant, observable changes. Dissociative recombination of H$_3$O$^+$, water adsorption and desorption onto dust grains, and ultraviolet photolysis of water and related species are found to be the three dominant processes regulating the gas-phase water abundance. While the changes are found to be significant, we find that the effect on gas phase water abundances throughout the disk is short-lived (days). Even though we do not see a substantial increase in long term water (gas and ice) production, the flares' large effects may be detectable as time varying inner disk water 'bursts' at radii between 5 and 30 au with future far infrared observations.
We present a parallel implicit-explicit time integration scheme for the advection-diffusion-reaction systems arising from the equations governing low-Mach number combustion with complex chemistry. Our strategy employs parallelization across the method to accelerate the serial Multi-Implicit Spectral Deferred Correction (MISDC) scheme used to couple the advection, diffusion, and reaction processes. In our approach, referred to as Concurrent Implicit Spectral Deferred Correction (CISDC), the diffusion solves and the reaction solves are performed concurrently by different processors. Our analysis shows that the proposed parallel scheme is stable for stiff problems and that the sweeps converge to the fixed-point solution at a faster rate than with serial MISDC. We present numerical examples to demonstrate that the new algorithm is high-order accurate in time, and achieves a parallel speedup compared to serial MISDC.
We studied the long-term dynamics of evolutionary Swarm Chemistry by extending the simulation length ten-fold compared to earlier work and by developing and using a new automated object harvesting method. Both macroscopic dynamics and microscopic object features were characterized and tracked using several measures. Results showed that the evolutionary dynamics tended to settle down into a stable state after the initial transient period, and that the extent of environmental perturbations also affected the evolutionary trends substantially. In the meantime, the automated harvesting method successfully produced a huge collection of spontaneously evolved objects, revealing the system's autonomous creativity at an unprecedented scale.
Interpretations of exoplanetary transmission spectra have been undermined by apparent obscuration due to clouds/hazes. Debate rages on whether weak H$_2$O features seen in exoplanet spectra are due to clouds or inherently depleted oxygen. Assertions of solar H$_2$O abundances have relied on making a priori model assumptions, e.g. chemical/radiative equilibrium. In this work, we attempt to address this problem with a new retrieval paradigm for transmission spectra. We introduce POSEIDON, a two-dimensional atmospheric retrieval algorithm including generalised inhomogeneous clouds. We demonstrate that this prescription allows one to break vital degeneracies between clouds and prominent molecular abundances. We apply POSEIDON to the best transmission spectrum presently available, for the hot Jupiter HD 209458b, uncovering new insights into its atmosphere at the day-night terminator. We extensively explore the parameter space with an unprecedented 10$^8$ models, spanning the continuum from fully cloudy to cloud-free atmospheres, in a fully Bayesian retrieval framework. We report the first detection of nitrogen chemistry (NH$_3$ and/or HCN) in an exoplanet atmosphere at 3.7-7.7$σ$ confidence, non-uniform cloud coverage at 4.5-5.4$σ$, high-altitude hazes at $>$3$σ$, and sub-solar H$_2$O at $\gtrsim$3-5$σ$, depending on the assumed cloud distribution. We detect NH$_3$ at 3.3$σ$ and 4.9$σ$ for fully cloudy and cloud-free scenarios, respectively. For the model with the highest Bayesian evidence, we constrain H$_2$O at 5-15 ppm (0.01-0.03$\times$ solar) and NH$_3$ at 0.01-2.7 ppm, strongly suggesting disequilibrium chemistry and cautioning against equilibrium assumptions. Our results herald new promise for retrieving cloudy atmospheres using high-precision HST and JWST spectra.
We have tested the original interaction-strength-interpolation (ISI) exchange-correlation functional for main group chemistry. The ISI functional is based on an interpolation between the weak and strong coupling limits and includes exact-exchange as well as the Görling-Levy second-order energy. We have analyzed in detail the basis-set dependence of the ISI functional, its dependence on the ground-state orbitals, and the influence of the size-consistency problem. We show and explain some of the expected limitations of the ISI functional (i.e. for atomization energies), but also unexpected results, such as the good performance for the interaction energy of dispersion-bonded complexes when the ISI correlation is used as a correction to Hartree-Fock.
AbstractThe role of physical organic chemistry (POC) within organic chemistry is portrayed from the personal point of view of the author. The paper compares the past (golden era) of POC with the present situation and considers some options for the future.
David Hollenbach, M. J. Kaufman, D. Neufeld
et al.
We model the production of OH+, H2O+, and H3O+ in interstellar clouds, using a steady state photodissociation region code that treats the freeze-out of gas species, grain surface chemistry, and desorption of ices from grains. The code includes PAHs, which have important effects on the chemistry. All three ions generally have two peaks in abundance as a function of depth into the cloud, one at A_V<~1 and one at A_V~3-8, the exact values depending on the ratio of incident ultraviolet flux to gas density. For relatively low values of the incident far ultraviolet flux on the cloud (χ<~ 1000; χ= 1= local interstellar value), the columns of OH+ and H2O+ scale roughly as the cosmic ray primary ionization rate ζ(crp) divided by the hydrogen nucleus density n. The H3O+ column is dominated by the second peak, and we show that if PAHs are present, N(H3O+) ~ 4x10^{13} cm^{-2} independent of ζ(crp) or n. If there are no PAHs or very small grains at the second peak, N(H3O+) can attain such columns only if low ionization potential metals are heavily depleted. We also model diffuse and translucent clouds in the interstellar medium, and show how observations of N(OH+)/N(H) and N(OH+)/N(H2O+) can be used to estimate ζ(crp)/n, χ/n and A_V in them. We compare our models to Herschel observations of these two ions, and estimate ζ(crp) ~ 4-6 x 10^-16 (n/100 cm^-3) s^-1 and χ/n = 0.03 cm^3 for diffuse foreground clouds towards W49N.