Experimental evidence of negative linear compressibility in the MIL-53 metal–organic framework family

International audienc

Lightshow: a Python package for generating computational x-ray absorption spectroscopy input files

First-principles computational spectroscopy is a critical tool for interpreting experiment, performing structure refinement, and developing new physical understanding. Systematically setting up input files for different simulation codes and a diverse class of materials is a challenging task with a very high barrier-to-entry, given the complexities and nuances of each individual simulation package. This task is non-trivial even for experts in the electronic structure field and nearly formidable for non-expert researchers. Lightshow solves this problem by providing a uniform abstraction for writing computational x-ray spectroscopy input files for multiple popular codes, including FEFF, VASP, OCEAN, EXCITING and XSPECTRA. Its extendable framework will also allow the community to easily add new functions and to incorporate new simulation codes.Comment: 3 pages, 1 figure, software can be found open source under the BSD-3-clause license at https://github.com/AI-multimodal/Lightsho

Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles

As machine learning (ML) methods continue to be applied to a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty aware machine learning methods have been used in select applications, but largely for scalar properties. In this work, we showcase an exemplary study in which neural network ensembles are used to predict the X-ray absorption spectra of small molecules, as well as their point-wise uncertainty, from local atomic environments. The performance of the resulting surrogate clearly demonstrates quantitative correlation between errors relative to ground truth and the predicted uncertainty estimates. Significantly, the model provides an upper bound on the expected error. Specifically, an important quality of this uncertainty-aware model is that it can indicate when the model is predicting on out-of-sample data. This allows for its integration with large scale sampling of structures together with active learning or other techniques for structure refinement. Additionally, our models can be generalized to larger molecules than those used for training, and also successfully track uncertainty due to random distortions in test molecules. While we demonstrate this workflow on a specific example, ensemble learning is completely general. We believe it could have significant impact on ML-enabled forward modeling of a broad array of molecular and materials properties.Comment: 24 pages, 16 figure

Highly selective oxygen reduction to hydrogen peroxide on transition metal single atom coordination.

Shifting electrochemical oxygen reduction towards 2e- pathway to hydrogen peroxide (H2O2), instead of the traditional 4e- to water, becomes increasingly important as a green method for H2O2 generation. Here, through a flexible control of oxygen reduction pathways on different transition metal single atom coordination in carbon nanotube, we discovered Fe-C-O as an efficient H2O2 catalyst, with an unprecedented onset of 0.822 V versus reversible hydrogen electrode in 0.1 M KOH to deliver 0.1 mA cm-2 H2O2 current, and a high H2O2 selectivity of above 95% in both alkaline and neutral pH. A wide range tuning of 2e-/4e- ORR pathways was achieved via different metal centers or neighboring metalloid coordination. Density functional theory calculations indicate that the Fe-C-O motifs, in a sharp contrast to the well-known Fe-C-N for 4e-, are responsible for the H2O2 pathway. This iron single atom catalyst demonstrated an effective water disinfection as a representative application

Enhanced superconductivity and electron correlations in intercalated ZrTe3

Charge density waves (CDWs) with superconductivity, competing Fermi surface instabilities, and collective orders have captured much interest in two-dimensional van der Waals (vdW) materials. Understanding the CDW suppression mechanism, its connection to the emerging superconducting state, and electronic correlations provides opportunities for engineering the electronic properties of vdW heterostructures and thin-film devices. Using a combination of the thermal transport, x-ray photoemission spectroscopy, Raman measurements, and first-principles calculations, we observe an increase in electronic correlations of the conducting states as the CDW is suppressed in ZrTe3 with 5% Cu and Ni intercalation in the vdW gap. As superconductivity emerges, intercalation brings not only decoupling of quasi-one-dimensional conduction electrons with phonons as a consequence of intercalation-induced lattice expansion but also a drastic increase in Zr2+ at the expense of Zr4+ metal atoms. These observations not only demonstrate the potential of atomic intercalates in the vdW gap for ground-state tuning but also illustrate the crucial role of the Zr metal valence in the formation of collective electronic orders

Unusual Electrical Conductivity Driven by Localized Stoichiometry Modification at Vertical Epitaxial Interfaces

Precise control of lattice mismatch accommodation and cation interdiffusion across the interface is critical to modulate correlated functionalities in epitaxial heterostructures, particularly when the interface composition is positioned near a compositional phase transition boundary. Here we select La1-xSrxMnO3 (LSMO) as a prototypical phase transition material and establish vertical epitaxial interfaces with NiO to explore the strong interplay between strain accommodation, stoichiometry modification, and localized electron transport across the interface. It is found that localized stoichiometry modification overcomes the plaguing dead layer problem in LSMO and leads to strongly directional conductivity, as manifested by more than three orders of magnitude difference between out-of-plane to in-plane conductivity. Comprehensive structural characterization and transport measurements reveal that this emerging behavior is related to a compositional change produced by directional cation diffusion that pushes the LSMO phase transition from insulating into metallic within an ultrathin interface region. This study explores the nature of unusual electric conductivity at vertical epitaxial interfaces and establishes an effective route for engineering nanoscale electron transport for oxide electronics

In situ characterization of mesoporous Co/CeO2 catalysts for the high-temperature water-gas shift

Mesoporous Co/CeO2 catalysts were found to exhibit significant activity for the high-temperature water-gas shift (WGS) reaction with cobalt loadings as low as 1 wt %. The catalysts feature a uniform dispersion of cobalt within the CeO2 fluorite type lattice with no evidence of discrete cobalt phase segregation. In situ XANES and ambient pressure XPS experiments were used to elucidate the active state of the catalysts as partially reduced cerium oxide doped with oxidized cobalt atoms. In situ XRD and DRIFTS experiments suggest facile cerium reduction and oxygen vacancy formation, particularly with lower cobalt loadings. In situ DRIFTS analysis also revealed the presence of surface carbonate and bidentate formate species under reaction conditions, which may be associated with additional mechanistic pathways for the WGS reaction. Deactivation behavior was observed with higher cobalt loadings. XANES data suggest the formation of small metallic cobalt clusters at temperatures above 400 °C may be responsible. Notably, this deactivation was not observed for the 1% cobalt loaded catalyst, which exhibited the highest activity per unit of cobalt.Peer ReviewedPostprint (author's final draft