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Larson, Daniel T.

Publications and source records attributed to Larson, Daniel T..

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Artificial Intelligence Guided Studies of van der Waals Magnets

A materials informatics framework to explore a large number of candidate van der Waals (vdW) materials is developed. In particular, in this study a large space of monolayer transition metal halides is investigated by combining high-throughput density functional theory calculations and artificial intelligence (AI) to accelerate the discovery of stable materials and the prediction of their magnetic properties. Here, the formation energy is used as a proxy for chemical stability. Semi-supervised learning is harnessed to mitigate the challenges of sparsely labeled materials data in order to improve the performance of AI models. This approach creates avenues for the rapid discovery of chemically stable vdW magnets by leveraging the ability of AI to recognize patterns in data, to learn mathematical representations of materials from data and to predict materials properties. Using this approach, previously unexplored vdW magnetic materials with potential applications in data storage and spintronics are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Low-energy moiré phonons in twisted bilayer van der Waals heterostructures

We develop a low-energy continuum model for phonons in twisted moiré bilayers, based on a configuration-space approach. In this approach, interatomic force constants are obtained from density functional theory (DFT) calculations of untwisted bilayers with various in-plane shifts. This allows for efficient computation of phonon properties for any small twist angle, while maintaining DFT-level accuracy. Based on this framework, we show how the low-energy phonon modes, including interlayer shearing and layer-breathing modes, vary with the twist angle. As the twist angle decreases, the frequencies of the low-energy modes are reordered and the atomic displacement fields corresponding to phonon eigenmodes break translational symmetry, developing periodicity on the moiré length scale. Here, we demonstrate the capabilities of our model by calculating the phonon properties of three specific structures: bilayer graphene, bilayer molybdenum disulfide (MoS 2 ), and molybdenum diselenide-tungsten diselenide (MoSe 2 -WSe 2 ).

2-dimensional systems↗
DOE OSTI2021

Microscopic origin of the high thermoelectric figure of merit of n -doped SnSe

Excellent thermoelectric performance in the out-of-layer n-doped SnSe has been observed experimentally [Chang et al., Science 360, 778-783 (2018)]. Though, a first-principles investigation of the dominant scattering mechanisms governing all thermoelectric transport properties is lacking. In the present work, by applying extensive first-principles calculations of electron-phonon coupling associated with parameterized calculation of the scattering by ionized impurities, we investigate the reasons behind the superior figure of merit as well as the enhancement of zT above 600 K in n-doped out-of-layer SnSe, as compared to p-doped SnSe with similar carrier densities. For the n-doped case, the relaxation time is dominated by ionized impurity scattering and increases with temperature, a feature that maintains the power factor at high values at higher temperatures and simultaneously causes the carrier thermal conductivity at zero electric current (κ el ) to decrease faster for higher temperatures, leading to an ultrahigh-zT = 3.1 at 807 K. We rationalize the roles played by κ el and κ 0 (the thermal conductivity due to carrier transport under isoelectrochemical conditions) in the determination of zT. Our results show the ratio between κ 0 and the lattice thermal conductivity indeed corresponds to the upper limit for zT, whereas the difference between calculated zT and the upper limit is proportional to κ el .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Data-driven studies of magnetic two-dimensional materials

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A 2 B 2 X 6 , based on the known material Cr 2 Ge 2 Te 6 , using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability. We show that machine learning tools, combined with DFT calculations, can provide a computationally efficient means to predict properties of such two-dimensional (2D) magnetic materials. Our data analytics approach provides insights into the microscopic origins of magnetic ordering in these systems. For instance, we find that the X site strongly affects the magnetic coupling between neighboring A sites, which drives the magnetic ordering. Our approach opens new ways for rapid discovery of chemically stable vdW materials that exhibit magnetic behavior.

42 ENGINEERING↗