Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy.

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. We also show that including multiple XANES modalities improves predictions notably on average, demonstrating a "multimodal advantage" over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.

Physically interpretable approximations of many-body spectral functions.

The rational function approximation provides a natural and interpretable representation of response functions such as the many-body spectral functions. We apply the vector fitting (VFIT) algorithm to fit a variety of spectral functions calculated from the Holstein model of electron-phonon interactions. We show that the resulting rational functions are highly efficient in their fitting of sharp features in the spectral functions, and could provide a means to infer physically relevant information from a spectral data set. The position of the peaks in the approximated spectral function are determined by the location of poles in the complex plane. In addition, we developed a variant of VFIT that incorporates regularization to improve the quality of fits. With this procedure, we demonstrate it is possible to achieve accurate spectral function fits that vary smoothly as a function of physical conditions.

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes.

X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 34, 6702 (2022). The XAS database is based on simulations using the excited electron and core-hole pseudopotential approach implemented in the Vienna Ab initio Simulation Package. Our database contains 2681 S K-edge XAS spectra for 66 crystalline and glassy structure models, making it the largest collection of first-principles computational XAS spectra for glass/ceramic lithium thiophosphates to date. This database can be used to correlate S spectral features with distinct S species based on their local coordination and short-range ordering in sulfide-based solid electrolytes. The data is openly distributed via the Materials Cloud, allowing researchers to access it for free and use it for further analysis, such as spectral fingerprinting, matching with experiments, and developing machine learning models.

Competition between energy- and entropy-driven activation in glasses.

In simplified models of glasses we clarify the existence of two different kinds of coexisting activated dynamics, with one of the two dominating over the other. One is the energy barrier hopping that is typically used to understand activation, and the other, which we call entropic activation, is driven by the scarcity of convenient directions in phase space. When entropic activation dominates, the height of the energy barriers is no longer the primary factor governing the system's slowdown. In our analysis, dominance of one mechanism over the other depends on temperature and the shape of the density of states. We also find that at low temperatures a phase transition between the two kinds of activation can occur. Our observations are used to provide a scenario that can harmonize the facilitation and thermodynamic pictures of the slowdown of glasses into a single description.

Effective traplike activated dynamics in a continuous landscape.

We use a simple model to extend network models for activated dynamics to a continuous landscape with a well-defined notion of distance and a direct connection to many-body systems. The model consists of a tracer in a high-dimensional funnel landscape with no disorder. We find a nonequilibrium low-temperature phase with aging dynamics that is effectively equivalent to that of models with built-in disorder, such as the trap model, step model and random energy model. Finally, we compare entropy with energy-driven activation, and we remark that the former is more robust to the choice of the dynamics since it does not depend on whether one uses local or global updates.

Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy.

Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof of principle, we demonstrate that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra of molecules to quantitative accuracy. Specifically, the predicted spectra reproduce nearly all prominent peaks, with 90% of the predicted peak locations within 1 eV of the ground truth. Besides its own utility in spectral analysis and structure inference, our method can be combined with structure search algorithms to enable high-throughput spectrum sampling of the vast material configuration space, which opens up new pathways to material design and discovery.

Microscopic model of the doping dependence of linewidths in monolayer transition metal dichalcogenides.

A fully microscopic model of the doping-dependent exciton and trion linewidths in the absorption spectra of monolayer transition metal dichalcogenides in the low temperature and low-doping regime is explored. The approach is based on perturbation theory and avoids the use of phenomenological parameters. In the low-doping regime, we find that the trion linewidth is relatively insensitive to doping levels, while the exciton linewidth increases monotonically with doping. On the other hand, we argue that the trion linewidth shows a somewhat stronger temperature dependence. The magnitudes of the linewidths are likely to be masked by phonon scattering for T ≥ 20 K in encapsulated samples in the low-doping regime. We discuss the breakdown of perturbation theory, which should occur at relatively low-doping levels and low temperatures. Our work also paves the way toward understanding a variety of related scattering processes, including impact ionization and Auger scattering in clean 2D samples.

Crystal structures of (R S)-N-[(1R,2S)-2-benz-yloxy-1-(2,6-di-methyl-phen-yl)prop-yl]-2-methyl-propane-2-sulfinamide and (R S)-N-[(1S,2R)-2-benz-yloxy-1-(2,4,6-tri-methyl-phen-yl)prop-yl]-2-methyl-propane-2-sulfinamide: two related protected 1,2-amino alcohols.

The title compounds, C22H31NO2S, (1), and C23H33NO2S, (2), are related protected 1,2-amino alcohols. They differ in the substituents on the benzene ring, viz. 2,6-di-methyl-phenyl in (1) and 2,4,6-tri-methyl-phenyl in (2). The plane of the phenyl ring is inclined to that of the benzene ring by 28.52 (7)° in (1) and by 44.65 (19)° in (2). In the crystal of (1), N-H⋯O=S and C-H⋯O=S hydrogen bonds link mol-ecules, forming chains along [100], while in (2), similar hydrogen bonds link mol-ecules into chains along [010]. The absolute structures of both compounds were determined by resonance scattering.