Computational vibrational spectroscopy beyond the harmonic approximation relies on the molecular potential and ideally dipole and possibly higher moments of charge distributions. In the past decade, there has been a paradigm shift in generating highly accurate Machine-Learned potentials (MLPs). These are precise fits to thousands of electronic energies, using modern methods of regression. With such MLPs, it is possible to combine these with a variety of post-harmonic quantum methods ranging from perturbation theory to full variational calculations. After a short review of these methods, we focus on vibrational self-consistent field and configuration interaction (VSCF + VCI) calculations, as implemented in the code MULTIMODE. Two applications of this software to complex parts of the infrared spectra of formic acid dimer and the protonated oxalate anion are presented. Two new interfaces to MULTIMODE are then given. One is a Python-based GUI to enable user-friendly input to MULTIMODE. The second interface, PyFort, which is written in Fortran, uses MLPs written in Python in MULTIMODE via a C wrapper. Demonstrations of this are given for a PhysNet potential of Meuwly and co-workers for protonated oxalate anion (C2O4H-) and for the "universal" force field MACE-OFF of Csányi and co-workers. MULTIMODE VSCF + VCI vibrational energies of C2O4H- using the PhysNet MLP agree well with those using a permutationally invariant potential, trained on the datasets used to train the PhysNet MLP. A test of the MACE-OFF interface is done for H2CO. The PyFort software for both these examples is provided in the supplementary material.

We present the electro-thermal characterization of transition-edge sensor (TES) detectors suspended on Si membranes fabricated using a silicon-on-insulator (SOI) wafer. The use of an all-silicon fabrication platform, in contrast to the more commonly used silicon nitride membranes, is compatible with monolithic fabrication of integrated TES and SQUID circuits. The all-silicon architecture additionally allows efficient use of focal plane area; the readout circuitry may be positioned out of the focal plane by bending a thinned portion of the chip. Compatibility with integrated fabrication and efficient use of focal plane area provide a path to an efficient soft X-ray spectrometer. This work is motivated by our goal to develop a 10,000-pixel TES spectrometer to overcome critical measurement limitations in catalysis research. The characterization of fragile, carbon-based intermediates via techniques like Resonant Inelastic X-ray Scattering (RIXS) is often precluded by the slow, high-flux nature of existing technologies. The new instrument will allow for fast RIXS measurements to be made without causing sample damage. We verify the detector models and measure the energy resolution using a pulsed optical laser, demonstrating the viability of this approach for the final instrument to be deployed at the National Synchrotron Light Source II (NSLS-II).

Folding and unfolding in molecules as simple as short hydrocarbons and as complicated as large proteins continue to be an active research field. Here, we investigate folding in n-C14H30 using both density functional theory (DFT)/B3LYP calculations of 27 772 local minima and a kinetic transition network calculated for a previously reported potential energy surface (PES) obtained by fitting roughly 250 000 B3LYP energies. In addition to generating a database of minima and the transition states that connect them, these calculations and the PES based on them have been used to develop a simple and accurate model for the energy landscape. The model for the local minima is based on the number of gauche torsions as well as their interactions with neighbors and next-nearest neighbors, resulting in three parameters, which are fitted using the direct DFT results. The transition states are governed by 13 parameters based on differences between the two connected local minima. The model predicts that there are 44 530 local minima (not counting permutation-inversion isomers) connected by 525 028 transition states, including degenerate rearrangements. When compared to the actual stationary points, it achieves a minimum absolute energy error of 43 cm-1 for the local minima and 47 cm-1 for the transition state barriers connecting levels of different energy. The model also provides accurate predictions of the most kinetically relevant folding and unfolding pathways, for example, from very highly excited configurations to the global minimum. In addition, it facilitates the determination of a disconnectivity graph using the Cambridge Energy Landscapes programs.

Hydrocarbons are ubiquitous as fuels, solvents, lubricants, and as the principal components of plastics and fibers, yet our ability to predict their dynamical properties is limited to force-field mechanics. Here, we report two machine-learned potential energy surfaces (PESs) for the linear 44-atom hydrocarbon C14H30 using an extensive data set of roughly 250,000 density functional theory (DFT) (B3LYP) energies for a large variety of configurations, obtained using MM3 direct-dynamics calculations at 500, 1000, and 2500 K. The surfaces, based on Permutationally Invariant Polynomials (PIPs) and using both a many-body expansion approach and a fragmented-basis approach, produce precise fits for energies and forces and also produce excellent out-of-sample agreement with direct DFT calculations for torsional and dihedral angle potentials. Going beyond precision, the PESs are used in molecular dynamics calculations that demonstrate the robustness of the PESs for a large range of conformations. The many-body PIPs PES, although more compute intensive than the fragmented-basis one, is directly transferable for other linear hydrocarbons.

We describe a nonlinear Er:doped fiber amplification scheme for producing fiber-delivered, 44 fs pulses with 100 kW peak power at arbitrary (0-60 MHz) repetition rate. Subsequent spectral broadening produces 80 THz of bandwidth.

Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality experimental data to predict three molecular properties (Kováts retention index, normal boiling point, and mass spectrum), using the same GNN architecture. We show that graph representations of molecules, combined with deep learning methodologies and high-quality data sets, lead to accurate machine learning models to predict molecular properties.

NeXUS is one of the NSF mid-scale facilities, which major mission is to make the cutting-edge attosecond technology available to the scientific community.

In this project we set out to make technical advances in time-resolved photoemission from surfaces and study molecule-surface charge transfer processes. Combining a novel light source developed at Stony Brook with a new type of time-of-flight photoemission electron microscope (TOF k-mic) developed in Mainz, Germany, we achieved the former goal. Our light source, based on cavity-enhanced high harmonic generation at 61 MHz repetition rate, enables ultrafast time-resolved photoemission experiments to be conducted with orders of magnitude higher photocurrents than otherwise possible. Critically, this provides the high dynamic range necessary to study perturbatively excited samples, such that one can study the intrinsic dynamics of electrons and holes as they would occur in a device. Combining our light source with the TOF k-mic, we’ve achieved an overall improvement in data rate of time- and angle-resolved photoemission (tr-ARPES) of approximately four orders of magnitude over the previous state-of-the-art using kHz-repetition rate sources and conventional hemispherical electron analyzers. This new instrument is now being used in experiments on 2D materials and organic semiconductors.

The toxicity and bioavailability of arsenic is heavily dependent on its speciation. Therefore, robust and accurate methods are needed to determine arsenic speciation profiles for materials related to public health initiatives, such as food safety. Here, X-ray spectroscopies are attractive candidates as they provide in situ, nondestructive analyses of solid samples without perturbation to the arsenic species therein. This work provides a speciation analysis for three certified reference materials for the food chemistry community, whose assigned values may be used to assess the merit of the X-ray spectroscopy results. Furthermore, extracts of SRM 3232 Kelp Powder, which is value-assigned for arsenic species, are measured to provide further evidence of its efficacy. These analyses are performed on the results of As K-edge X-ray Absorption Near Edge Structure (XANES) measurements collected on each sample. Notably, such analyses have traditionally relied on linear combination fitting of a minimal subset of empirical standards selected by stepwise regression. This is known to be problematic for compounds with meaningfully collinear spectra and can yield overestimates of the accuracy of the analysis. Therefore, the least absolute shrinkage and selection operator (lasso) regression method is used to reduce the risk of overfitting and increase the interpretability of statistical inferences. As this is a biased statistical method, results and uncertainties are estimated using a bootstrap method accounting for the dominant sources of variability. Finally, this method does not separate model and data selection from regression analysis. Indeed, a survey of many spectral influences is presented including changes in the: state of methylation, state of protonation, oxidation state, coordination geometry, and sample phase. These compounds were all included in the model's training set, preventing model over-simplification and enabling high-throughput and robust analyses.

We provide updates on design and progress of a novel attosecond XUV user facility. This facility will enable characterization of molecular and material systems with attosecond, angstrom, and chemical state resolution; the first of its kind in the US.

Using tunable frequency combs and enhancement cavities, we present a method for transient absorption spectroscopy in molecular beams in the UV/vis spectral region.

The Kováts retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independent of many experimental variables and, as such, is considered a near-universal descriptor of retention time on a chromatography column. The Kováts retention indices of a large number of molecules have been determined experimentally. The "NIST 20: GC Method/Retention Index Library" database has collected and, more importantly, curated retention indices of a subset of these compounds resulting in a highly valued reference database. The experimental data in the library form an ideal data set for training machine learning models for the prediction of retention indices of unknown compounds. In this article, we describe the training of a graph neural network model to predict the Kováts retention index for compounds in the NIST library and compare this approach with previous work [1]. We predict the Kováts retention index with a mean unsigned error of 28 index units as compared to 44, the putative best result using a convolutional neural network [1]. The NIST library also incorporates an estimation scheme based on a group contribution approach that achieves a mean unsigned error of 114 compared to the experimental data. Our method uses the same input data source as the group contribution approach, making its application straightforward and convenient to apply to existing libraries. Our results convincingly demonstrate the predictive powers of systematic, data-driven approaches leveraging deep learning methodologies applied to chemical data and for the data in the NIST 20 library outperform previous models.

We report a simple but detailed solution 13C nuclear magnetic resonance spectroscopic study of atomically precise neutral Au25(SR)180 (SR = alkyl thiolate) clusters. The paramagnetic 13C Knight shift of alkyl chain carbons, which is proportional to the local electron spin density, exhibits an electron spin delocalization that exponentially decays along the alkyl chain. The magnitude and decay constant of the observed electron spin delocalization, although largely independent of alkyl chain length, depend on where, that is, "in" versus "out" (vide infra) position, the alkyl chain is bound, in agreement with density functional theory calculations. Notably, the determined position-dependent decay constants, 1.70/Å and 0.41/Å for "in" and "out" ligands, respectively, not only could have important ramifications in molecular spintronics but are also comparable to measured decay constants in molecular electrical conductance of alkyl chains, potentially offering an alternative, simple method for estimating the latter. Moreover, the negative intercept temperatures of linear fits of reciprocal 13C (as well its bound 1H) Knight shift versus temperature strongly suggest the existence of local ferrimagnetism in individual Au25(SR)180 clusters.

Developing a fundamental understanding of charge transport in organic semiconductors has been a decades-long challenge that hinders performance improvement. In particular, recent work suggests that organic semiconductors have unique charge–phonon interactions where phonons temporarily interrupt the band structure causing a transient localization of charge carriers. Despite growing evidence to support this mechanism, further understanding and control will depend on pinpointing the molecular motions that cause substantial change to the band structure. Here we combine experimental and theoretical techniques to demonstrate the phonon energies and associated molecular motions governing the charge–phonon interaction in single crystal tetracene. We investigate phonon properties using polarized Raman spectroscopy, transmission electron microscopy, and density functional theory, and correlate this with the anisotropic mobility. We find that specific phonons disrupt the band orbital in the high-mobility direction, evident in the discrepancy between measured and static calculations of the mobility anisotropy ratio in tetracene.

An X-ray free-electron laser oscillator (XFELO) is a new type of hard X-ray source that would produce fully coherent pulses with meV bandwidth and stable intensity. The XFELO complements existing sources based on self-amplified spontaneous emission (SASE) from high-gain X-ray free-electron lasers (XFEL) that produce ultra-short pulses with broad-band chaotic spectra. This report is based on discussions of scientific opportunities enabled by an XFELO during a workshop held at SLAC on June 29 - July 1, 2016

A combined in situ electrochemical attenuated total reflection-surface enhanced IR absorption spectroscopy, microkinetic simulation, and density functional theory calculation study shows that not only can the adsorbed sulfide disproportionally affect the surface binding of OOH* (EOOH* ) vs OH* (EOH* ), i.e., breaking the original scaling relationship of pure metals (Ir, Pd, Pt, Au), to enhance oxygen reduction reaction (ORR) activity but can also be used as a reaction pathway alternating species to help deepen our mechanistic understanding of ORR.