SCALE method for single-cell ATAC-seq analysis via latent feature extraction.

Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Design of a Graphene Nitrene Two-Dimensional Catalyst Heterostructure Providing a Well-Defined Site Accommodating 1 to 3 Metals, with Application to CO₂ Reduction Electrocatalysis for the 2 Metal Case

Recently, the reduction of CO₂ to fuels has been the subject of numerous studies, but the selectivity and activity remain inadequate. Progress has been made on single-site two-dimensional catalysts based on graphene coupled to a metal and nitrogen for the CO₂ reduction reaction (CO₂RR); however, the product is usually CO, and the metal–N environment remains ambiguous. We report a novel two-dimensional graphene nitrene heterostructure (grafiN₆) providing well-defined active sites (N₆) that can bind one to three metals for the CO₂RR. We find that homobimetallic FeFe–grafiN₆ could reduce CO₂ to CH₄ at −0.61 V and to CH₃CH₂OH at −0.68 V versus reversible hydrogen electrode, with high product selectivity. Moreover, the heteronuclear FeCu–grafiN₆ system may be significantly less affected by hydrogen evolution reaction, while maintaining a low limiting potential (−0.68 V) for C1 and C2 mechanisms. Binding metals to one N₆ site but not the other could promote efficient electron transport facilitating some reaction steps. This framework for single or multiple metal sites might also provide unique catalytic sites for other catalytic processes

Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screening

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H₂ and O₂ is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe₂/WTe₂ with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting

Infrared spectroscopy of small-molecule endofullerenes

Hydrogen is one of the few molecules which has been incarcerated in the molecular cage of C$_{60}$ and forms endohedral supramolecular complex H$_2$@C$_{60}$. In this confinement hydrogen acquires new properties. Its translational motion becomes quantized and is correlated with its rotations. We applied infrared spectroscopy to study the dynamics of hydrogen isotopologs H$_2$, D$_2$ and HD incarcerated in C$_{60}$. The translational and rotational modes appear as side bands to the hydrogen vibrational mode in the mid infrared part of the absorption spectrum. Because of the large mass difference of hydrogen and C$_{60}$ and the high symmetry of C$_{60}$ the problem is identical to a problem of a vibrating rotor moving in a three-dimensional spherical potential. The translational motion within the C$_{60}$ cavity breaks the inversion symmetry and induces optical activity of H$_2$. We derive potential, rotational, vibrational and dipole moment parameters from the analysis of the infrared absorption spectra. Our results were used to derive the parameters of a pairwise additive five-dimensional potential energy surface for H$_2$@C$_{60}$. The same parameters were used to predict H$_2$ energies inside C$_{70}$[Xu et al., J. Chem. Phys., {\bf 130}, 224306 (2009)]. We compare the predicted energies and the low temperature infrared absorption spectra of H$_2$@C$_{70}$.Comment: Updated author lis