Virtual node graph neural network for full phonon prediction.

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility.

Tetris-inspired detector with neural network for radiation mapping.

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Topological Superconductors from a Materials Perspective.

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (nonuniversal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this Perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSCs, focusing on how a system is identified as a TSC candidate and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates.

Machine learning magnetism classifiers from atomic coordinates.

The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement protocols, while computational approaches such as first-principles density functional theory (DFT) need additional semi-empirical correction, and reliable prediction is still largely limited to collinear magnetism. Here, we present a machine learning model that aims to classify the magnetic structure by inputting atomic coordinates containing transition metal and rare earth elements. By building a Euclidean equivariant neural network that preserves the crystallographic symmetry, the magnetic structure (ferromagnetic, antiferromagnetic, and non-magnetic) and magnetic propagation vector (zero or non-zero) can be predicted with an average accuracy of 77.8% and 73.6%. In particular, a 91% accuracy is reached when predicting no magnetic ordering even if the structure contains magnetic element(s). Our work represents one step forward to solving the grand challenge of full magnetic structure determination.

Efficient solvent suppression with adiabatic inversion for 1H-detected solid-state NMR.

This study introduces a conceptually new solvent suppression scheme with adiabatic inversion pulses for 1H-detected multidimensional solid-state NMR (SSNMR) of biomolecules and other systems, which is termed "Solvent suppression of Liquid signal with Adiabatic Pulse" (SLAP). 1H-detected 2D 13C/1H SSNMR data of uniformly 13C- and 15N-labeled GB1 sample using ultra-fast magic angle spinning at a spinning rate of 60 kHz demonstrated that the SLAP scheme showed up to 3.5-fold better solvent suppression performance over a traditional solvent-suppression scheme for SSNMR, MISSISSIPPI (Zhou and Rienstra, J Magn Reson 192:167-172, 2008) with 2/3 of the average RF power.

A conductive metal-organic framework photoanode.

We report the development of photosensitizing arrays based on conductive metal-organic frameworks (MOFs) that enable light harvesting and efficient charge separation. Zn2TTFTB (TTFTB = tetrathiafulvalene tetrabenzoate) MOFs are deposited directly onto TiO2 photoanodes and structurally characterized by pXRD and EXAFS measurements. Photoinduced interfacial charge transfer dynamics are investigated by combining time-resolved THz spectroscopy and quantum dynamics simulations. Sub-600 fs electron injection into TiO2 is observed for Zn2TTFTB-TiO2 and is compared to the corresponding dynamics for TTFTB-TiO2 analogues that lack the extended MOF architecture. Rapid electron injection from the MOF into TiO2 is enhanced by facile migration of the hole away from the interfacial region. Holes migrate through strongly coupled HOMO orbitals localized on the tetrathiafulvalene cores of the columnar stacks of the MOF, whereas electrons are less easily transferred through the spiral staircase arrangement of phenyl substituents of the MOF. The reported findings suggest that conductive MOFs could be exploited as novel photosensitizing arrays in applications to slow, and thereby make difficult, photocatalytic reactions such as those required for water-splitting in artificial photosynthesis.

Metal-Organic Framework Photoconductivity via Time-Resolved Terahertz Spectroscopy.

While metal-organic frameworks (MOFs) have been under thorough investigation over the past two decades, photoconductive MOFs are an emerging class of materials with promising applications in light harvesting and photocatalysis. To date, there is not a general method to investigate the photoconductivity of polycrystalline MOF samples as-prepared. Herein, we utilize time-resolved terahertz spectroscopy along with a new sample preparation method to determine the photoconductivity of Zn2TTFTB, an archetypical conductive MOF, in a noncontact manner. Using this technique, we were able to gain insight into MOF photoconductivity dynamics with subpicosecond resolution, revealing two distinct carrier lifetimes of 0.6 and 31 ps and a long-lived component of several ns. Additionally, we determined the frequency dependent photoconductivity of Zn2TTFTB which was shown to follow Drude-Smith behavior. Such insights are crucially important with regard to developing the next generation of functional photoconductive MOF materials.